{
 "cells": [
  {
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   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "execution": {
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     "iopub.status.busy": "2022-02-27T13:17:28.117978Z",
     "iopub.status.idle": "2022-02-27T13:17:28.331605Z",
     "shell.execute_reply": "2022-02-27T13:17:28.330862Z",
     "shell.execute_reply.started": "2022-02-27T13:17:28.118123Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data80164\n"
     ]
    }
   ],
   "source": [
    "# 查看当前挂载的数据集目录, 该目录下的变更重启环境后会自动还原\n",
    "# View dataset directory. \n",
    "# This directory will be recovered automatically after resetting environment. \n",
    "!ls /home/aistudio/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2022-02-27T13:17:28.333824Z",
     "iopub.status.busy": "2022-02-27T13:17:28.333291Z",
     "iopub.status.idle": "2022-02-27T13:17:28.545080Z",
     "shell.execute_reply": "2022-02-27T13:17:28.544505Z",
     "shell.execute_reply.started": "2022-02-27T13:17:28.333791Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data  ocrnet_check  result\n"
     ]
    }
   ],
   "source": [
    "# 查看工作区文件, 该目录下的变更将会持久保存. 请及时清理不必要的文件, 避免加载过慢.\n",
    "# View personal work directory. \n",
    "# All changes under this directory will be kept even after reset. \n",
    "# Please clean unnecessary files in time to speed up environment loading. \n",
    "!ls /home/aistudio/work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2022-02-27T13:17:28.546431Z",
     "iopub.status.busy": "2022-02-27T13:17:28.545955Z",
     "iopub.status.idle": "2022-02-27T13:17:30.663075Z",
     "shell.execute_reply": "2022-02-27T13:17:30.662278Z",
     "shell.execute_reply.started": "2022-02-27T13:17:28.546404Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: 无法创建目录\"/home/aistudio/external-libraries\": 文件已存在\n",
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Collecting beautifulsoup4\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/69/bf/f0f194d3379d3f3347478bd267f754fc68c11cbf2fe302a6ab69447b1417/beautifulsoup4-4.10.0-py3-none-any.whl (97 kB)\n",
      "     |████████████████████████████████| 97 kB 7.7 MB/s             \n",
      "\u001b[?25hCollecting soupsieve>1.2\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/72/a6/fd01694427f1c3fcadfdc5f1de901b813b9ac756f0806ef470cfed1de281/soupsieve-2.3.1-py3-none-any.whl (37 kB)\n",
      "Installing collected packages: soupsieve, beautifulsoup4\n",
      "Successfully installed beautifulsoup4-4.10.0 soupsieve-2.3.1\n",
      "\u001b[33mWARNING: Target directory /home/aistudio/external-libraries/soupsieve-2.3.1.dist-info already exists. Specify --upgrade to force replacement.\u001b[0m\n",
      "\u001b[33mWARNING: Target directory /home/aistudio/external-libraries/beautifulsoup4-4.10.0.dist-info already exists. Specify --upgrade to force replacement.\u001b[0m\n",
      "\u001b[33mWARNING: Target directory /home/aistudio/external-libraries/soupsieve already exists. Specify --upgrade to force replacement.\u001b[0m\n",
      "\u001b[33mWARNING: Target directory /home/aistudio/external-libraries/bs4 already exists. Specify --upgrade to force replacement.\u001b[0m\n",
      "\u001b[33mWARNING: You are using pip version 21.3.1; however, version 22.0.3 is available.\n",
      "You should consider upgrading via the '/opt/conda/envs/python35-paddle120-env/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# 如果需要进行持久化安装, 需要使用持久化路径, 如下方代码示例:\n",
    "# If a persistence installation is required, \n",
    "# you need to use the persistence path as the following: \n",
    "!mkdir /home/aistudio/external-libraries\n",
    "!pip install beautifulsoup4 -t /home/aistudio/external-libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2022-02-27T13:17:30.664911Z",
     "iopub.status.busy": "2022-02-27T13:17:30.664508Z",
     "iopub.status.idle": "2022-02-27T13:17:30.669477Z",
     "shell.execute_reply": "2022-02-27T13:17:30.668925Z",
     "shell.execute_reply.started": "2022-02-27T13:17:30.664881Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 同时添加如下代码, 这样每次环境(kernel)启动的时候只要运行下方代码即可: \n",
    "# Also add the following code, \n",
    "# so that every time the environment (kernel) starts, \n",
    "# just run the following code: \n",
    "import sys \n",
    "sys.path.append('/home/aistudio/external-libraries')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "一、项目背景介绍\n",
    "遥感影像地块分割, 旨在对遥感影像进行像素级内容解析，对遥感影像中感兴趣的类别进行提取和分类，在城乡规划、防汛救灾等领域具有很高的实用价值，在工业界也受到了广泛关注。现有的遥感影像地块分割数据处理方法局限于特定的场景和特定的数据来源，且精度无法满足需求。因此在实际应用中，仍然大量依赖于人工处理，需要消耗大量的人力、物力、财力。本项目旨在衡量遥感影像地块分割模型在多个类别（如建筑、道路、林地等）上的效果，利用人工智能技术，对多来源、多场景的异构遥感影像数据进行充分挖掘，打造高效、实用的算法，提高遥感影像的分析提取能力。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-27T13:17:30.670709Z",
     "iopub.status.busy": "2022-02-27T13:17:30.670444Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".\n",
      "├── data\n",
      "│   └── data80164\n",
      "│       ├── img_testA\n",
      "│       │   ├── 10000.jpg\n",
      "│       │   ├── 1000.jpg\n",
      "│       │   ├── 1003.jpg\n",
      "│       │   ├── 1006.jpg\n",
      "│       │   ├── 1008.jpg\n",
      "│       │   ├── 1009.jpg\n",
      "│       │   ├── 1013.jpg\n",
      "│       │   ├── 1015.jpg\n",
      "│       │   ├── 1016.jpg\n",
      "│       │   ├── 1018.jpg\n",
      "│       │   ├── 1020.jpg\n",
      "│       │   ├── 1021.jpg\n",
      "│       │   ├── 1022.jpg\n",
      "│       │   ├── 1026.jpg\n",
      "│       │   ├── 1027.jpg\n",
      "│       │   ├── 1029.jpg\n",
      "│       │   ├── 1030.jpg\n",
      "│       │   ├── 1031.jpg\n",
      "│       │   ├── 1034.jpg\n",
      "│       │   ├── 1035.jpg\n",
      "│       │   ├── 1037.jpg\n",
      "│       │   ├── 1038.jpg\n",
      "│       │   ├── 1040.jpg\n",
      "│       │   ├── 1042.jpg\n",
      "│       │   ├── 1043.jpg\n",
      "│       │   ├── 1044.jpg\n",
      "│       │   ├── 1046.jpg\n",
      "│       │   ├── 1047.jpg\n",
      "│       │   ├── 1048.jpg\n",
      "│       │   ├── 104.jpg\n",
      "│       │   ├── 1050.jpg\n",
      "│       │   ├── 1052.jpg\n",
      "│       │   ├── 1055.jpg\n",
      "│       │   ├── 1056.jpg\n",
      "│       │   ├── 105.jpg\n",
      "│       │   ├── 1060.jpg\n",
      "│       │   ├── 1061.jpg\n",
      "│       │   ├── 1063.jpg\n",
      "│       │   ├── 1067.jpg\n",
      "│       │   ├── 1069.jpg\n",
      "│       │   ├── 106.jpg\n",
      "│       │   ├── 1070.jpg\n",
      "│       │   ├── 1073.jpg\n",
      "│       │   ├── 1074.jpg\n",
      "│       │   ├── 1075.jpg\n",
      "│       │   ├── 1076.jpg\n",
      "│       │   ├── 1078.jpg\n",
      "│       │   ├── 1079.jpg\n",
      "│       │   ├── 107.jpg\n",
      "│       │   ├── 1081.jpg\n",
      "│       │   ├── 1083.jpg\n",
      "│       │   ├── 1084.jpg\n",
      "│       │   ├── 1088.jpg\n",
      "│       │   ├── 1090.jpg\n",
      "│       │   ├── 1092.jpg\n",
      "│       │   ├── 10.jpg\n",
      "│       │   ├── 1100.jpg\n",
      "│       │   ├── 1102.jpg\n",
      "│       │   ├── 1104.jpg\n",
      "│       │   ├── 1107.jpg\n",
      "│       │   ├── 1111.jpg\n",
      "│       │   ├── 1115.jpg\n",
      "│       │   ├── 1116.jpg\n",
      "│       │   ├── 1118.jpg\n",
      "│       │   ├── 111.jpg\n",
      "│       │   ├── 1121.jpg\n",
      "│       │   ├── 1122.jpg\n",
      "│       │   ├── 1124.jpg\n",
      "│       │   ├── 1125.jpg\n",
      "│       │   ├── 1127.jpg\n",
      "│       │   ├── 1130.jpg\n",
      "│       │   ├── 1132.jpg\n",
      "│       │   ├── 1133.jpg\n",
      "│       │   ├── 1138.jpg\n",
      "│       │   ├── 1141.jpg\n",
      "│       │   ├── 1142.jpg\n",
      "│       │   ├── 1145.jpg\n",
      "│       │   ├── 1148.jpg\n",
      "│       │   ├── 1150.jpg\n",
      "│       │   ├── 1151.jpg\n",
      "│       │   ├── 1154.jpg\n",
      "│       │   ├── 1158.jpg\n",
      "│       │   ├── 115.jpg\n",
      "│       │   ├── 1160.jpg\n",
      "│       │   ├── 1161.jpg\n",
      "│       │   ├── 1166.jpg\n",
      "│       │   ├── 1167.jpg\n",
      "│       │   ├── 1172.jpg\n",
      "│       │   ├── 1173.jpg\n",
      "│       │   ├── 1175.jpg\n",
      "│       │   ├── 1178.jpg\n",
      "│       │   ├── 1181.jpg\n",
      "│       │   ├── 1182.jpg\n",
      "│       │   ├── 1184.jpg\n",
      "│       │   ├── 1188.jpg\n",
      "│       │   ├── 1189.jpg\n",
      "│       │   ├── 118.jpg\n",
      "│       │   ├── 1190.jpg\n",
      "│       │   ├── 1191.jpg\n",
      "│       │   ├── 1192.jpg\n",
      "│       │   ├── 1194.jpg\n",
      "│       │   ├── 1197.jpg\n",
      "│       │   ├── 1198.jpg\n",
      "│       │   ├── 1199.jpg\n",
      "│       │   ├── 119.jpg\n",
      "│       │   ├── 1202.jpg\n",
      "│       │   ├── 1204.jpg\n",
      "│       │   ├── 1205.jpg\n",
      "│       │   ├── 1209.jpg\n",
      "│       │   ├── 1210.jpg\n",
      "│       │   ├── 1212.jpg\n",
      "│       │   ├── 1214.jpg\n",
      "│       │   ├── 1215.jpg\n",
      "│       │   ├── 1216.jpg\n",
      "│       │   ├── 121.jpg\n",
      "│       │   ├── 1220.jpg\n",
      "│       │   ├── 1225.jpg\n",
      "│       │   ├── 1227.jpg\n",
      "│       │   ├── 122.jpg\n",
      "│       │   ├── 1230.jpg\n",
      "│       │   ├── 1231.jpg\n",
      "│       │   ├── 1232.jpg\n",
      "│       │   ├── 1233.jpg\n",
      "│       │   ├── 1234.jpg\n",
      "│       │   ├── 1235.jpg\n",
      "│       │   ├── 1238.jpg\n",
      "│       │   ├── 1244.jpg\n",
      "│       │   ├── 1245.jpg\n",
      "│       │   ├── 1246.jpg\n",
      "│       │   ├── 1249.jpg\n",
      "│       │   ├── 1250.jpg\n",
      "│       │   ├── 1252.jpg\n",
      "│       │   ├── 1254.jpg\n",
      "│       │   ├── 1255.jpg\n",
      "│       │   ├── 1264.jpg\n",
      "│       │   ├── 1266.jpg\n",
      "│       │   ├── 1269.jpg\n",
      "│       │   ├── 1270.jpg\n",
      "│       │   ├── 1275.jpg\n",
      "│       │   ├── 1276.jpg\n",
      "│       │   ├── 1279.jpg\n",
      "│       │   ├── 127.jpg\n",
      "│       │   ├── 1281.jpg\n",
      "│       │   ├── 1282.jpg\n",
      "│       │   ├── 1283.jpg\n",
      "│       │   ├── 1286.jpg\n",
      "│       │   ├── 1288.jpg\n",
      "│       │   ├── 1291.jpg\n",
      "│       │   ├── 1292.jpg\n",
      "│       │   ├── 1296.jpg\n",
      "│       │   ├── 1297.jpg\n",
      "│       │   ├── 129.jpg\n",
      "│       │   ├── 12.jpg\n",
      "│       │   ├── 1300.jpg\n",
      "│       │   ├── 1301.jpg\n",
      "│       │   ├── 1302.jpg\n",
      "│       │   ├── 1303.jpg\n",
      "│       │   ├── 1307.jpg\n",
      "│       │   ├── 1308.jpg\n",
      "│       │   ├── 1309.jpg\n",
      "│       │   ├── 1311.jpg\n",
      "│       │   ├── 1312.jpg\n",
      "│       │   ├── 1313.jpg\n",
      "│       │   ├── 1315.jpg\n",
      "│       │   ├── 1317.jpg\n",
      "│       │   ├── 1321.jpg\n",
      "│       │   ├── 1323.jpg\n",
      "│       │   ├── 1324.jpg\n",
      "│       │   ├── 1325.jpg\n",
      "│       │   ├── 1326.jpg\n",
      "│       │   ├── 1328.jpg\n",
      "│       │   ├── 1329.jpg\n",
      "│       │   ├── 132.jpg\n",
      "│       │   ├── 1331.jpg\n",
      "│       │   ├── 1332.jpg\n",
      "│       │   ├── 1333.jpg\n",
      "│       │   ├── 1334.jpg\n",
      "│       │   ├── 1336.jpg\n",
      "│       │   ├── 1337.jpg\n",
      "│       │   ├── 1339.jpg\n",
      "│       │   ├── 1340.jpg\n",
      "│       │   ├── 1341.jpg\n",
      "│       │   ├── 1342.jpg\n",
      "│       │   ├── 1345.jpg\n",
      "│       │   ├── 1346.jpg\n",
      "│       │   ├── 1347.jpg\n",
      "│       │   ├── 1348.jpg\n",
      "│       │   ├── 1349.jpg\n",
      "│       │   ├── 134.jpg\n",
      "│       │   ├── 1355.jpg\n",
      "│       │   ├── 1356.jpg\n",
      "│       │   ├── 1359.jpg\n",
      "│       │   ├── 1360.jpg\n",
      "│       │   ├── 1362.jpg\n",
      "│       │   ├── 1369.jpg\n",
      "│       │   ├── 1370.jpg\n",
      "│       │   ├── 1371.jpg\n",
      "│       │   ├── 1373.jpg\n",
      "│       │   ├── 1376.jpg\n",
      "│       │   ├── 1377.jpg\n",
      "│       │   ├── 1378.jpg\n",
      "│       │   ├── 1379.jpg\n",
      "│       │   ├── 137.jpg\n",
      "│       │   ├── 1380.jpg\n",
      "│       │   ├── 1382.jpg\n",
      "│       │   ├── 1383.jpg\n",
      "│       │   ├── 1384.jpg\n",
      "│       │   ├── 1387.jpg\n",
      "│       │   ├── 1388.jpg\n",
      "│       │   ├── 1393.jpg\n",
      "│       │   ├── 1397.jpg\n",
      "│       │   ├── 1398.jpg\n",
      "│       │   ├── 1399.jpg\n",
      "│       │   ├── 13.jpg\n",
      "│       │   ├── 1402.jpg\n",
      "│       │   ├── 1403.jpg\n",
      "│       │   ├── 1404.jpg\n",
      "│       │   ├── 1406.jpg\n",
      "│       │   ├── 1408.jpg\n",
      "│       │   ├── 140.jpg\n",
      "│       │   ├── 1411.jpg\n",
      "│       │   ├── 1412.jpg\n",
      "│       │   ├── 1413.jpg\n",
      "│       │   ├── 1414.jpg\n",
      "│       │   ├── 1417.jpg\n",
      "│       │   ├── 1420.jpg\n",
      "│       │   ├── 1421.jpg\n",
      "│       │   ├── 1422.jpg\n",
      "│       │   ├── 1424.jpg\n",
      "│       │   ├── 1425.jpg\n",
      "│       │   ├── 1426.jpg\n",
      "│       │   ├── 1431.jpg\n",
      "│       │   ├── 1432.jpg\n",
      "│       │   ├── 1435.jpg\n",
      "│       │   ├── 1437.jpg\n",
      "│       │   ├── 1439.jpg\n",
      "│       │   ├── 143.jpg\n",
      "│       │   ├── 1440.jpg\n",
      "│       │   ├── 1442.jpg\n",
      "│       │   ├── 1443.jpg\n",
      "│       │   ├── 1448.jpg\n",
      "│       │   ├── 1449.jpg\n",
      "│       │   ├── 144.jpg\n",
      "│       │   ├── 1452.jpg\n",
      "│       │   ├── 1453.jpg\n",
      "│       │   ├── 1462.jpg\n",
      "│       │   ├── 1464.jpg\n",
      "│       │   ├── 1465.jpg\n",
      "│       │   ├── 1466.jpg\n",
      "│       │   ├── 1467.jpg\n",
      "│       │   ├── 1471.jpg\n",
      "│       │   ├── 1473.jpg\n",
      "│       │   ├── 1474.jpg\n",
      "│       │   ├── 1475.jpg\n",
      "│       │   ├── 1476.jpg\n",
      "│       │   ├── 1478.jpg\n",
      "│       │   ├── 1479.jpg\n",
      "│       │   ├── 1481.jpg\n",
      "│       │   ├── 1482.jpg\n",
      "│       │   ├── 1487.jpg\n",
      "│       │   ├── 1491.jpg\n",
      "│       │   ├── 1494.jpg\n",
      "│       │   ├── 1497.jpg\n",
      "│       │   ├── 1498.jpg\n",
      "│       │   ├── 1499.jpg\n",
      "│       │   ├── 149.jpg\n",
      "│       │   ├── 14.jpg\n",
      "│       │   ├── 1502.jpg\n",
      "│       │   ├── 1507.jpg\n",
      "│       │   ├── 1508.jpg\n",
      "│       │   ├── 1510.jpg\n",
      "│       │   ├── 1512.jpg\n",
      "│       │   ├── 1513.jpg\n",
      "│       │   ├── 1516.jpg\n",
      "│       │   ├── 1518.jpg\n",
      "│       │   ├── 1521.jpg\n",
      "│       │   ├── 1522.jpg\n",
      "│       │   ├── 1523.jpg\n",
      "│       │   ├── 1524.jpg\n",
      "│       │   ├── 1525.jpg\n",
      "│       │   ├── 1526.jpg\n",
      "│       │   ├── 1527.jpg\n",
      "│       │   ├── 152.jpg\n",
      "│       │   ├── 1532.jpg\n",
      "│       │   ├── 1534.jpg\n",
      "│       │   ├── 1536.jpg\n",
      "│       │   ├── 1537.jpg\n",
      "│       │   ├── 1538.jpg\n",
      "│       │   ├── 153.jpg\n",
      "│       │   ├── 1540.jpg\n",
      "│       │   ├── 1542.jpg\n",
      "│       │   ├── 1543.jpg\n",
      "│       │   ├── 1547.jpg\n",
      "│       │   ├── 1548.jpg\n",
      "│       │   ├── 154.jpg\n",
      "│       │   ├── 1550.jpg\n",
      "│       │   ├── 1551.jpg\n",
      "│       │   ├── 1552.jpg\n",
      "│       │   ├── 1553.jpg\n",
      "│       │   ├── 1558.jpg\n",
      "│       │   ├── 155.jpg\n",
      "│       │   ├── 1561.jpg\n",
      "│       │   ├── 1563.jpg\n",
      "│       │   ├── 1565.jpg\n",
      "│       │   ├── 1567.jpg\n",
      "│       │   ├── 1569.jpg\n",
      "│       │   ├── 156.jpg\n",
      "│       │   ├── 1571.jpg\n",
      "│       │   ├── 1573.jpg\n",
      "│       │   ├── 1575.jpg\n",
      "│       │   ├── 1578.jpg\n",
      "│       │   ├── 1579.jpg\n",
      "│       │   ├── 1581.jpg\n",
      "│       │   ├── 1582.jpg\n",
      "│       │   ├── 1584.jpg\n",
      "│       │   ├── 1585.jpg\n",
      "│       │   ├── 1588.jpg\n",
      "│       │   ├── 1589.jpg\n",
      "│       │   ├── 1596.jpg\n",
      "│       │   ├── 1598.jpg\n",
      "│       │   ├── 1599.jpg\n",
      "│       │   ├── 159.jpg\n",
      "│       │   ├── 1600.jpg\n",
      "│       │   ├── 1602.jpg\n",
      "│       │   ├── 1606.jpg\n",
      "│       │   ├── 1608.jpg\n",
      "│       │   ├── 1609.jpg\n",
      "│       │   ├── 1610.jpg\n",
      "│       │   ├── 1612.jpg\n",
      "│       │   ├── 1613.jpg\n",
      "│       │   ├── 1614.jpg\n",
      "│       │   ├── 1615.jpg\n",
      "│       │   ├── 1616.jpg\n",
      "│       │   ├── 1619.jpg\n",
      "│       │   ├── 1620.jpg\n",
      "│       │   ├── 1622.jpg\n",
      "│       │   ├── 1623.jpg\n",
      "│       │   ├── 1624.jpg\n",
      "│       │   ├── 1625.jpg\n",
      "│       │   ├── 1626.jpg\n",
      "│       │   ├── 1629.jpg\n",
      "│       │   ├── 1633.jpg\n",
      "│       │   ├── 1636.jpg\n",
      "│       │   ├── 1637.jpg\n",
      "│       │   ├── 1638.jpg\n",
      "│       │   ├── 1646.jpg\n",
      "│       │   ├── 1647.jpg\n",
      "│       │   ├── 1648.jpg\n",
      "│       │   ├── 1649.jpg\n",
      "│       │   ├── 1650.jpg\n",
      "│       │   ├── 1651.jpg\n",
      "│       │   ├── 1653.jpg\n",
      "│       │   ├── 1659.jpg\n",
      "│       │   ├── 165.jpg\n",
      "│       │   ├── 1660.jpg\n",
      "│       │   ├── 1663.jpg\n",
      "│       │   ├── 1664.jpg\n",
      "│       │   ├── 1665.jpg\n",
      "│       │   ├── 1666.jpg\n",
      "│       │   ├── 1667.jpg\n",
      "│       │   ├── 1668.jpg\n",
      "│       │   ├── 1669.jpg\n",
      "│       │   ├── 1670.jpg\n",
      "│       │   ├── 1671.jpg\n",
      "│       │   ├── 1674.jpg\n",
      "│       │   ├── 1676.jpg\n",
      "│       │   ├── 1677.jpg\n",
      "│       │   ├── 1678.jpg\n",
      "│       │   ├── 1679.jpg\n",
      "│       │   ├── 1680.jpg\n",
      "│       │   ├── 1685.jpg\n",
      "│       │   ├── 1687.jpg\n",
      "│       │   ├── 168.jpg\n",
      "│       │   ├── 1690.jpg\n",
      "│       │   ├── 1693.jpg\n",
      "│       │   ├── 1695.jpg\n",
      "│       │   ├── 1697.jpg\n",
      "│       │   ├── 1698.jpg\n",
      "│       │   ├── 1699.jpg\n",
      "│       │   ├── 169.jpg\n",
      "│       │   ├── 16.jpg\n",
      "│       │   ├── 1700.jpg\n",
      "│       │   ├── 1701.jpg\n",
      "│       │   ├── 1704.jpg\n",
      "│       │   ├── 1705.jpg\n",
      "│       │   ├── 1707.jpg\n",
      "│       │   ├── 1709.jpg\n",
      "│       │   ├── 170.jpg\n",
      "│       │   ├── 1711.jpg\n",
      "│       │   ├── 1712.jpg\n",
      "│       │   ├── 1717.jpg\n",
      "│       │   ├── 1720.jpg\n",
      "│       │   ├── 1722.jpg\n",
      "│       │   ├── 1724.jpg\n",
      "│       │   ├── 1725.jpg\n",
      "│       │   ├── 1728.jpg\n",
      "│       │   ├── 1732.jpg\n",
      "│       │   ├── 1733.jpg\n",
      "│       │   ├── 1737.jpg\n",
      "│       │   ├── 1738.jpg\n",
      "│       │   ├── 1739.jpg\n",
      "│       │   ├── 1740.jpg\n",
      "│       │   ├── 1741.jpg\n",
      "│       │   ├── 1742.jpg\n",
      "│       │   ├── 1743.jpg\n",
      "│       │   ├── 1746.jpg\n",
      "│       │   ├── 1747.jpg\n",
      "│       │   ├── 1749.jpg\n",
      "│       │   ├── 1753.jpg\n",
      "│       │   ├── 1754.jpg\n",
      "│       │   ├── 1755.jpg\n",
      "│       │   ├── 1756.jpg\n",
      "│       │   ├── 1758.jpg\n",
      "│       │   ├── 1760.jpg\n",
      "│       │   ├── 1761.jpg\n",
      "│       │   ├── 1763.jpg\n",
      "│       │   ├── 1765.jpg\n",
      "│       │   ├── 1768.jpg\n",
      "│       │   ├── 176.jpg\n",
      "│       │   ├── 1770.jpg\n",
      "│       │   ├── 1772.jpg\n",
      "│       │   ├── 1774.jpg\n",
      "│       │   ├── 1776.jpg\n",
      "│       │   ├── 1778.jpg\n",
      "│       │   ├── 177.jpg\n",
      "│       │   ├── 1782.jpg\n",
      "│       │   ├── 1784.jpg\n",
      "│       │   ├── 1786.jpg\n",
      "│       │   ├── 1787.jpg\n",
      "│       │   ├── 1788.jpg\n",
      "│       │   ├── 1795.jpg\n",
      "│       │   ├── 1797.jpg\n",
      "│       │   ├── 1798.jpg\n",
      "│       │   ├── 1801.jpg\n",
      "│       │   ├── 1804.jpg\n",
      "│       │   ├── 1805.jpg\n",
      "│       │   ├── 1808.jpg\n",
      "│       │   ├── 180.jpg\n",
      "│       │   ├── 1810.jpg\n",
      "│       │   ├── 1811.jpg\n",
      "│       │   ├── 1814.jpg\n",
      "│       │   ├── 1815.jpg\n",
      "│       │   ├── 1816.jpg\n",
      "│       │   ├── 1818.jpg\n",
      "│       │   ├── 1819.jpg\n",
      "│       │   ├── 181.jpg\n",
      "│       │   ├── 1820.jpg\n",
      "│       │   ├── 1821.jpg\n",
      "│       │   ├── 1822.jpg\n",
      "│       │   ├── 1823.jpg\n",
      "│       │   ├── 1824.jpg\n",
      "│       │   ├── 1825.jpg\n",
      "│       │   ├── 1826.jpg\n",
      "│       │   ├── 1828.jpg\n",
      "│       │   ├── 1829.jpg\n",
      "│       │   ├── 182.jpg\n",
      "│       │   ├── 1835.jpg\n",
      "│       │   ├── 1837.jpg\n",
      "│       │   ├── 1838.jpg\n",
      "│       │   ├── 1840.jpg\n",
      "│       │   ├── 1841.jpg\n",
      "│       │   ├── 1842.jpg\n",
      "│       │   ├── 1845.jpg\n",
      "│       │   ├── 1847.jpg\n",
      "│       │   ├── 1848.jpg\n",
      "│       │   ├── 184.jpg\n",
      "│       │   ├── 1851.jpg\n",
      "│       │   ├── 1854.jpg\n",
      "│       │   ├── 1855.jpg\n",
      "│       │   ├── 1856.jpg\n",
      "│       │   ├── 1859.jpg\n",
      "│       │   ├── 1860.jpg\n",
      "│       │   ├── 1861.jpg\n",
      "│       │   ├── 1862.jpg\n",
      "│       │   ├── 1864.jpg\n",
      "│       │   ├── 1865.jpg\n",
      "│       │   ├── 1866.jpg\n",
      "│       │   ├── 1867.jpg\n",
      "│       │   ├── 1868.jpg\n",
      "│       │   ├── 1869.jpg\n",
      "│       │   ├── 1875.jpg\n",
      "│       │   ├── 1876.jpg\n",
      "│       │   ├── 187.jpg\n",
      "│       │   ├── 1880.jpg\n",
      "│       │   ├── 1881.jpg\n",
      "│       │   ├── 1883.jpg\n",
      "│       │   ├── 1884.jpg\n",
      "│       │   ├── 1887.jpg\n",
      "│       │   ├── 1892.jpg\n",
      "│       │   ├── 1895.jpg\n",
      "│       │   ├── 1898.jpg\n",
      "│       │   ├── 1899.jpg\n",
      "│       │   ├── 189.jpg\n",
      "│       │   ├── 1902.jpg\n",
      "│       │   ├── 1905.jpg\n",
      "│       │   ├── 1906.jpg\n",
      "│       │   ├── 1907.jpg\n",
      "│       │   ├── 1909.jpg\n",
      "│       │   ├── 190.jpg\n",
      "│       │   ├── 1911.jpg\n",
      "│       │   ├── 1912.jpg\n",
      "│       │   ├── 1913.jpg\n",
      "│       │   ├── 1914.jpg\n",
      "│       │   ├── 1915.jpg\n",
      "│       │   ├── 1920.jpg\n",
      "│       │   ├── 1925.jpg\n",
      "│       │   ├── 1928.jpg\n",
      "│       │   ├── 192.jpg\n",
      "│       │   ├── 1930.jpg\n",
      "│       │   ├── 1931.jpg\n",
      "│       │   ├── 1933.jpg\n",
      "│       │   ├── 1936.jpg\n",
      "│       │   ├── 1937.jpg\n",
      "│       │   ├── 1942.jpg\n",
      "│       │   ├── 1943.jpg\n",
      "│       │   ├── 1945.jpg\n",
      "│       │   ├── 1946.jpg\n",
      "│       │   ├── 1947.jpg\n",
      "│       │   ├── 1951.jpg\n",
      "│       │   ├── 1954.jpg\n",
      "│       │   ├── 1955.jpg\n",
      "│       │   ├── 1956.jpg\n",
      "│       │   ├── 1959.jpg\n",
      "│       │   ├── 1961.jpg\n",
      "│       │   ├── 1963.jpg\n",
      "│       │   ├── 1964.jpg\n",
      "│       │   ├── 1967.jpg\n",
      "│       │   ├── 1968.jpg\n",
      "│       │   ├── 1969.jpg\n",
      "│       │   ├── 196.jpg\n",
      "│       │   ├── 1970.jpg\n",
      "│       │   ├── 1971.jpg\n",
      "│       │   ├── 1972.jpg\n",
      "│       │   ├── 1976.jpg\n",
      "│       │   ├── 1983.jpg\n",
      "│       │   ├── 1986.jpg\n",
      "│       │   ├── 1987.jpg\n",
      "│       │   ├── 1989.jpg\n",
      "│       │   ├── 1992.jpg\n",
      "│       │   ├── 1993.jpg\n",
      "│       │   ├── 1995.jpg\n",
      "│       │   ├── 1996.jpg\n",
      "│       │   ├── 1997.jpg\n",
      "│       │   ├── 1998.jpg\n",
      "│       │   ├── 2001.jpg\n",
      "│       │   ├── 2002.jpg\n",
      "│       │   ├── 2003.jpg\n",
      "│       │   ├── 2004.jpg\n",
      "│       │   ├── 2006.jpg\n",
      "│       │   ├── 2007.jpg\n",
      "│       │   ├── 2008.jpg\n",
      "│       │   ├── 200.jpg\n",
      "│       │   ├── 2014.jpg\n",
      "│       │   ├── 2015.jpg\n",
      "│       │   ├── 2016.jpg\n",
      "│       │   ├── 2017.jpg\n",
      "│       │   ├── 2019.jpg\n",
      "│       │   ├── 201.jpg\n",
      "│       │   ├── 2020.jpg\n",
      "│       │   ├── 2021.jpg\n",
      "│       │   ├── 2026.jpg\n",
      "│       │   ├── 2027.jpg\n",
      "│       │   ├── 2028.jpg\n",
      "│       │   ├── 2034.jpg\n",
      "│       │   ├── 2035.jpg\n",
      "│       │   ├── 2037.jpg\n",
      "│       │   ├── 2038.jpg\n",
      "│       │   ├── 2040.jpg\n",
      "│       │   ├── 2042.jpg\n",
      "│       │   ├── 2043.jpg\n",
      "│       │   ├── 2045.jpg\n",
      "│       │   ├── 2047.jpg\n",
      "│       │   ├── 2048.jpg\n",
      "│       │   ├── 2049.jpg\n",
      "│       │   ├── 2050.jpg\n",
      "│       │   ├── 2051.jpg\n",
      "│       │   ├── 2052.jpg\n",
      "│       │   ├── 2053.jpg\n",
      "│       │   ├── 2056.jpg\n",
      "│       │   ├── 2059.jpg\n",
      "│       │   ├── 2060.jpg\n",
      "│       │   ├── 2061.jpg\n",
      "│       │   ├── 2065.jpg\n",
      "│       │   ├── 2067.jpg\n",
      "│       │   ├── 2068.jpg\n",
      "│       │   ├── 2069.jpg\n",
      "│       │   ├── 2071.jpg\n",
      "│       │   ├── 2073.jpg\n",
      "│       │   ├── 2074.jpg\n",
      "│       │   ├── 2075.jpg\n",
      "│       │   ├── 2081.jpg\n",
      "│       │   ├── 2082.jpg\n",
      "│       │   ├── 2088.jpg\n",
      "│       │   ├── 2089.jpg\n",
      "│       │   ├── 2091.jpg\n",
      "│       │   ├── 20.jpg\n",
      "│       │   ├── 2102.jpg\n",
      "│       │   ├── 2104.jpg\n",
      "│       │   ├── 2106.jpg\n",
      "│       │   ├── 2110.jpg\n",
      "│       │   ├── 2111.jpg\n",
      "│       │   ├── 2112.jpg\n",
      "│       │   ├── 2115.jpg\n",
      "│       │   ├── 2117.jpg\n",
      "│       │   ├── 2119.jpg\n",
      "│       │   ├── 211.jpg\n",
      "│       │   ├── 2122.jpg\n",
      "│       │   ├── 2125.jpg\n",
      "│       │   ├── 2127.jpg\n",
      "│       │   ├── 2128.jpg\n",
      "│       │   ├── 2132.jpg\n",
      "│       │   ├── 2133.jpg\n",
      "│       │   ├── 2135.jpg\n",
      "│       │   ├── 2138.jpg\n",
      "│       │   ├── 2139.jpg\n",
      "│       │   ├── 2141.jpg\n",
      "│       │   ├── 2143.jpg\n",
      "│       │   ├── 2144.jpg\n",
      "│       │   ├── 2147.jpg\n",
      "│       │   ├── 2148.jpg\n",
      "│       │   ├── 214.jpg\n",
      "│       │   ├── 2150.jpg\n",
      "│       │   ├── 2151.jpg\n",
      "│       │   ├── 2155.jpg\n",
      "│       │   ├── 2158.jpg\n",
      "│       │   ├── 2162.jpg\n",
      "│       │   ├── 2163.jpg\n",
      "│       │   ├── 2165.jpg\n",
      "│       │   ├── 2167.jpg\n",
      "│       │   ├── 2168.jpg\n",
      "│       │   ├── 2171.jpg\n",
      "│       │   ├── 2172.jpg\n",
      "│       │   ├── 2173.jpg\n",
      "│       │   ├── 2174.jpg\n",
      "│       │   ├── 2175.jpg\n",
      "│       │   ├── 2177.jpg\n",
      "│       │   ├── 217.jpg\n",
      "│       │   ├── 2180.jpg\n",
      "│       │   ├── 2181.jpg\n",
      "│       │   ├── 2187.jpg\n",
      "│       │   ├── 218.jpg\n",
      "│       │   ├── 2194.jpg\n",
      "│       │   ├── 2195.jpg\n",
      "│       │   ├── 2197.jpg\n",
      "│       │   ├── 2199.jpg\n",
      "│       │   ├── 219.jpg\n",
      "│       │   ├── 21.jpg\n",
      "│       │   ├── 2200.jpg\n",
      "│       │   ├── 2201.jpg\n",
      "│       │   ├── 2205.jpg\n",
      "│       │   ├── 2207.jpg\n",
      "│       │   ├── 220.jpg\n",
      "│       │   ├── 2210.jpg\n",
      "│       │   ├── 2212.jpg\n",
      "│       │   ├── 2214.jpg\n",
      "│       │   ├── 2215.jpg\n",
      "│       │   ├── 2219.jpg\n",
      "│       │   ├── 221.jpg\n",
      "│       │   ├── 2223.jpg\n",
      "│       │   ├── 2225.jpg\n",
      "│       │   ├── 2226.jpg\n",
      "│       │   ├── 2228.jpg\n",
      "│       │   ├── 2233.jpg\n",
      "│       │   ├── 2238.jpg\n",
      "│       │   ├── 2243.jpg\n",
      "│       │   ├── 2244.jpg\n",
      "│       │   ├── 2247.jpg\n",
      "│       │   ├── 2248.jpg\n",
      "│       │   ├── 224.jpg\n",
      "│       │   ├── 2250.jpg\n",
      "│       │   ├── 2253.jpg\n",
      "│       │   ├── 2254.jpg\n",
      "│       │   ├── 2257.jpg\n",
      "│       │   ├── 2261.jpg\n",
      "│       │   ├── 2264.jpg\n",
      "│       │   ├── 2266.jpg\n",
      "│       │   ├── 2267.jpg\n",
      "│       │   ├── 2268.jpg\n",
      "│       │   ├── 226.jpg\n",
      "│       │   ├── 2272.jpg\n",
      "│       │   ├── 2274.jpg\n",
      "│       │   ├── 2279.jpg\n",
      "│       │   ├── 227.jpg\n",
      "│       │   ├── 2280.jpg\n",
      "│       │   ├── 2285.jpg\n",
      "│       │   ├── 2286.jpg\n",
      "│       │   ├── 2289.jpg\n",
      "│       │   ├── 2290.jpg\n",
      "│       │   ├── 2291.jpg\n",
      "│       │   ├── 2292.jpg\n",
      "│       │   ├── 2293.jpg\n",
      "│       │   ├── 2294.jpg\n",
      "│       │   ├── 2296.jpg\n",
      "│       │   ├── 2297.jpg\n",
      "│       │   ├── 2298.jpg\n",
      "│       │   ├── 2299.jpg\n",
      "│       │   ├── 2300.jpg\n",
      "│       │   ├── 2303.jpg\n",
      "│       │   ├── 2304.jpg\n",
      "│       │   ├── 2307.jpg\n",
      "│       │   ├── 230.jpg\n",
      "│       │   ├── 2312.jpg\n",
      "│       │   ├── 2314.jpg\n",
      "│       │   ├── 2316.jpg\n",
      "│       │   ├── 2317.jpg\n",
      "│       │   ├── 231.jpg\n",
      "│       │   ├── 2322.jpg\n",
      "│       │   ├── 2324.jpg\n",
      "│       │   ├── 2325.jpg\n",
      "│       │   ├── 2326.jpg\n",
      "│       │   ├── 232.jpg\n",
      "│       │   ├── 2331.jpg\n",
      "│       │   ├── 2333.jpg\n",
      "│       │   ├── 2334.jpg\n",
      "│       │   ├── 2338.jpg\n",
      "│       │   ├── 2339.jpg\n",
      "│       │   ├── 233.jpg\n",
      "│       │   ├── 2341.jpg\n",
      "│       │   ├── 2347.jpg\n",
      "│       │   ├── 2348.jpg\n",
      "│       │   ├── 2349.jpg\n",
      "│       │   ├── 234.jpg\n",
      "│       │   ├── 2350.jpg\n",
      "│       │   ├── 2351.jpg\n",
      "│       │   ├── 2352.jpg\n",
      "│       │   ├── 2356.jpg\n",
      "│       │   ├── 2358.jpg\n",
      "│       │   ├── 2359.jpg\n",
      "│       │   ├── 235.jpg\n",
      "│       │   ├── 2361.jpg\n",
      "│       │   ├── 2362.jpg\n",
      "│       │   ├── 2363.jpg\n",
      "│       │   ├── 2365.jpg\n",
      "│       │   ├── 2366.jpg\n",
      "│       │   ├── 2368.jpg\n",
      "│       │   ├── 2369.jpg\n",
      "│       │   ├── 2370.jpg\n",
      "│       │   ├── 2371.jpg\n",
      "│       │   ├── 2377.jpg\n",
      "│       │   ├── 237.jpg\n",
      "│       │   ├── 2380.jpg\n",
      "│       │   ├── 2383.jpg\n",
      "│       │   ├── 2385.jpg\n",
      "│       │   ├── 2390.jpg\n",
      "│       │   ├── 2392.jpg\n",
      "│       │   ├── 2393.jpg\n",
      "│       │   ├── 2395.jpg\n",
      "│       │   ├── 2398.jpg\n",
      "│       │   ├── 23.jpg\n",
      "│       │   ├── 2401.jpg\n",
      "│       │   ├── 2404.jpg\n",
      "│       │   ├── 2405.jpg\n",
      "│       │   ├── 2407.jpg\n",
      "│       │   ├── 2409.jpg\n",
      "│       │   ├── 2410.jpg\n",
      "│       │   ├── 2413.jpg\n",
      "│       │   ├── 2414.jpg\n",
      "│       │   ├── 2417.jpg\n",
      "│       │   ├── 2419.jpg\n",
      "│       │   ├── 241.jpg\n",
      "│       │   ├── 2420.jpg\n",
      "│       │   ├── 2421.jpg\n",
      "│       │   ├── 2422.jpg\n",
      "│       │   ├── 2423.jpg\n",
      "│       │   ├── 2424.jpg\n",
      "│       │   ├── 2427.jpg\n",
      "│       │   ├── 2428.jpg\n",
      "│       │   ├── 242.jpg\n",
      "│       │   ├── 2430.jpg\n",
      "│       │   ├── 2432.jpg\n",
      "│       │   ├── 2433.jpg\n",
      "│       │   ├── 2434.jpg\n",
      "│       │   ├── 2436.jpg\n",
      "│       │   ├── 2437.jpg\n",
      "│       │   ├── 2438.jpg\n",
      "│       │   ├── 2440.jpg\n",
      "│       │   ├── 2443.jpg\n",
      "│       │   ├── 2444.jpg\n",
      "│       │   ├── 2449.jpg\n",
      "│       │   ├── 2450.jpg\n",
      "│       │   ├── 2451.jpg\n",
      "│       │   ├── 2452.jpg\n",
      "│       │   ├── 2454.jpg\n",
      "│       │   ├── 2456.jpg\n",
      "│       │   ├── 2457.jpg\n",
      "│       │   ├── 2458.jpg\n",
      "│       │   ├── 2459.jpg\n",
      "│       │   ├── 245.jpg\n",
      "│       │   ├── 2460.jpg\n",
      "│       │   ├── 2466.jpg\n",
      "│       │   ├── 2467.jpg\n",
      "│       │   ├── 2468.jpg\n",
      "│       │   ├── 246.jpg\n",
      "│       │   ├── 2470.jpg\n",
      "│       │   ├── 2475.jpg\n",
      "│       │   ├── 2476.jpg\n",
      "│       │   ├── 2477.jpg\n",
      "│       │   ├── 2482.jpg\n",
      "│       │   ├── 2484.jpg\n",
      "│       │   ├── 2487.jpg\n",
      "│       │   ├── 2491.jpg\n",
      "│       │   ├── 2492.jpg\n",
      "│       │   ├── 2495.jpg\n",
      "│       │   ├── 2499.jpg\n",
      "│       │   ├── 24.jpg\n",
      "│       │   ├── 2500.jpg\n",
      "│       │   ├── 2502.jpg\n",
      "│       │   ├── 2505.jpg\n",
      "│       │   ├── 2506.jpg\n",
      "│       │   ├── 2507.jpg\n",
      "│       │   ├── 250.jpg\n",
      "│       │   ├── 2511.jpg\n",
      "│       │   ├── 2514.jpg\n",
      "│       │   ├── 2516.jpg\n",
      "│       │   ├── 2517.jpg\n",
      "│       │   ├── 2519.jpg\n",
      "│       │   ├── 251.jpg\n",
      "│       │   ├── 2523.jpg\n",
      "│       │   ├── 2524.jpg\n",
      "│       │   ├── 2525.jpg\n",
      "│       │   ├── 2526.jpg\n",
      "│       │   ├── 2527.jpg\n",
      "│       │   ├── 2529.jpg\n",
      "│       │   ├── 252.jpg\n",
      "│       │   ├── 2530.jpg\n",
      "│       │   ├── 2533.jpg\n",
      "│       │   ├── 2534.jpg\n",
      "│       │   ├── 2540.jpg\n",
      "│       │   ├── 2550.jpg\n",
      "│       │   ├── 2551.jpg\n",
      "│       │   ├── 2552.jpg\n",
      "│       │   ├── 2555.jpg\n",
      "│       │   ├── 2557.jpg\n",
      "│       │   ├── 255.jpg\n",
      "│       │   ├── 2561.jpg\n",
      "│       │   ├── 2563.jpg\n",
      "│       │   ├── 2564.jpg\n",
      "│       │   ├── 2566.jpg\n",
      "│       │   ├── 2567.jpg\n",
      "│       │   ├── 2569.jpg\n",
      "│       │   ├── 256.jpg\n",
      "│       │   ├── 2570.jpg\n",
      "│       │   ├── 2571.jpg\n",
      "│       │   ├── 2572.jpg\n",
      "│       │   ├── 2573.jpg\n",
      "│       │   ├── 2575.jpg\n",
      "│       │   ├── 2576.jpg\n",
      "│       │   ├── 257.jpg\n",
      "│       │   ├── 2582.jpg\n",
      "│       │   ├── 2584.jpg\n",
      "│       │   ├── 2586.jpg\n",
      "│       │   ├── 2591.jpg\n",
      "│       │   ├── 2592.jpg\n",
      "│       │   ├── 2598.jpg\n",
      "│       │   ├── 2601.jpg\n",
      "│       │   ├── 2602.jpg\n",
      "│       │   ├── 2603.jpg\n",
      "│       │   ├── 2608.jpg\n",
      "│       │   ├── 2609.jpg\n",
      "│       │   ├── 2611.jpg\n",
      "│       │   ├── 2612.jpg\n",
      "│       │   ├── 2613.jpg\n",
      "│       │   ├── 2614.jpg\n",
      "│       │   ├── 2616.jpg\n",
      "│       │   ├── 261.jpg\n",
      "│       │   ├── 2625.jpg\n",
      "│       │   ├── 2626.jpg\n",
      "│       │   ├── 262.jpg\n",
      "│       │   ├── 2639.jpg\n",
      "│       │   ├── 263.jpg\n",
      "│       │   ├── 2641.jpg\n",
      "│       │   ├── 2643.jpg\n",
      "│       │   ├── 2650.jpg\n",
      "│       │   ├── 2651.jpg\n",
      "│       │   ├── 2656.jpg\n",
      "│       │   ├── 2661.jpg\n",
      "│       │   ├── 2663.jpg\n",
      "│       │   ├── 2666.jpg\n",
      "│       │   ├── 2669.jpg\n",
      "│       │   ├── 2670.jpg\n",
      "│       │   ├── 2672.jpg\n",
      "│       │   ├── 2673.jpg\n",
      "│       │   ├── 2674.jpg\n",
      "│       │   ├── 2678.jpg\n",
      "│       │   ├── 2679.jpg\n",
      "│       │   ├── 2680.jpg\n",
      "│       │   ├── 2682.jpg\n",
      "│       │   ├── 2683.jpg\n",
      "│       │   ├── 2684.jpg\n",
      "│       │   ├── 2687.jpg\n",
      "│       │   ├── 2689.jpg\n",
      "│       │   ├── 2693.jpg\n",
      "│       │   ├── 2698.jpg\n",
      "│       │   ├── 2699.jpg\n",
      "│       │   ├── 2700.jpg\n",
      "│       │   ├── 2702.jpg\n",
      "│       │   ├── 2704.jpg\n",
      "│       │   ├── 2707.jpg\n",
      "│       │   ├── 2708.jpg\n",
      "│       │   ├── 2709.jpg\n",
      "│       │   ├── 270.jpg\n",
      "│       │   ├── 2714.jpg\n",
      "│       │   ├── 2715.jpg\n",
      "│       │   ├── 2717.jpg\n",
      "│       │   ├── 2719.jpg\n",
      "│       │   ├── 271.jpg\n",
      "│       │   ├── 2722.jpg\n",
      "│       │   ├── 2724.jpg\n",
      "│       │   ├── 2725.jpg\n",
      "│       │   ├── 2728.jpg\n",
      "│       │   ├── 2729.jpg\n",
      "│       │   ├── 272.jpg\n",
      "│       │   ├── 2730.jpg\n",
      "│       │   ├── 2731.jpg\n",
      "│       │   ├── 2732.jpg\n",
      "│       │   ├── 2735.jpg\n",
      "│       │   ├── 2738.jpg\n",
      "│       │   ├── 2739.jpg\n",
      "│       │   ├── 2741.jpg\n",
      "│       │   ├── 2745.jpg\n",
      "│       │   ├── 2748.jpg\n",
      "│       │   ├── 2750.jpg\n",
      "│       │   ├── 2752.jpg\n",
      "│       │   ├── 2753.jpg\n",
      "│       │   ├── 2755.jpg\n",
      "│       │   ├── 2756.jpg\n",
      "│       │   ├── 2757.jpg\n",
      "│       │   ├── 2759.jpg\n",
      "│       │   ├── 275.jpg\n",
      "│       │   ├── 2761.jpg\n",
      "│       │   ├── 2762.jpg\n",
      "│       │   ├── 2763.jpg\n",
      "│       │   ├── 2764.jpg\n",
      "│       │   ├── 2765.jpg\n",
      "│       │   ├── 2768.jpg\n",
      "│       │   ├── 276.jpg\n",
      "│       │   ├── 2770.jpg\n",
      "│       │   ├── 2774.jpg\n",
      "│       │   ├── 2776.jpg\n",
      "│       │   ├── 2779.jpg\n",
      "│       │   ├── 277.jpg\n",
      "│       │   ├── 2780.jpg\n",
      "│       │   ├── 2781.jpg\n",
      "│       │   ├── 2784.jpg\n",
      "│       │   ├── 2786.jpg\n",
      "│       │   ├── 278.jpg\n",
      "│       │   ├── 2790.jpg\n",
      "│       │   ├── 2791.jpg\n",
      "│       │   ├── 2792.jpg\n",
      "│       │   ├── 2793.jpg\n",
      "│       │   ├── 2795.jpg\n",
      "│       │   ├── 2796.jpg\n",
      "│       │   ├── 2797.jpg\n",
      "│       │   ├── 279.jpg\n",
      "│       │   ├── 27.jpg\n",
      "│       │   ├── 2813.jpg\n",
      "│       │   ├── 2814.jpg\n",
      "│       │   ├── 2815.jpg\n",
      "│       │   ├── 2816.jpg\n",
      "│       │   ├── 2819.jpg\n",
      "│       │   ├── 2825.jpg\n",
      "│       │   ├── 2826.jpg\n",
      "│       │   ├── 282.jpg\n",
      "│       │   ├── 2830.jpg\n",
      "│       │   ├── 2832.jpg\n",
      "│       │   ├── 2839.jpg\n",
      "│       │   ├── 283.jpg\n",
      "│       │   ├── 2842.jpg\n",
      "│       │   ├── 2844.jpg\n",
      "│       │   ├── 2846.jpg\n",
      "│       │   ├── 2848.jpg\n",
      "│       │   ├── 2853.jpg\n",
      "│       │   ├── 2854.jpg\n",
      "│       │   ├── 2858.jpg\n",
      "│       │   ├── 2859.jpg\n",
      "│       │   ├── 285.jpg\n",
      "│       │   ├── 2860.jpg\n",
      "│       │   ├── 2862.jpg\n",
      "│       │   ├── 2864.jpg\n",
      "│       │   ├── 2866.jpg\n",
      "│       │   ├── 2867.jpg\n",
      "│       │   ├── 2868.jpg\n",
      "│       │   ├── 2870.jpg\n",
      "│       │   ├── 2871.jpg\n",
      "│       │   ├── 2872.jpg\n",
      "│       │   ├── 2877.jpg\n",
      "│       │   ├── 287.jpg\n",
      "│       │   ├── 2880.jpg\n",
      "│       │   ├── 2881.jpg\n",
      "│       │   ├── 2882.jpg\n",
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      "│       │   ├── 2884.jpg\n",
      "│       │   ├── 2887.jpg\n",
      "│       │   ├── 2888.jpg\n",
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      "│       │   ├── 2891.jpg\n",
      "│       │   ├── 2892.jpg\n",
      "│       │   ├── 2897.jpg\n",
      "│       │   ├── 2899.jpg\n",
      "│       │   ├── 2902.jpg\n",
      "│       │   ├── 2909.jpg\n",
      "│       │   ├── 2912.jpg\n",
      "│       │   ├── 2913.jpg\n",
      "│       │   ├── 2914.jpg\n",
      "│       │   ├── 2915.jpg\n",
      "│       │   ├── 2916.jpg\n",
      "│       │   ├── 2918.jpg\n",
      "│       │   ├── 2919.jpg\n",
      "│       │   ├── 291.jpg\n",
      "│       │   ├── 2920.jpg\n",
      "│       │   ├── 2922.jpg\n",
      "│       │   ├── 2923.jpg\n",
      "│       │   ├── 2925.jpg\n",
      "│       │   ├── 2932.jpg\n",
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      "│       │   ├── 2934.jpg\n",
      "│       │   ├── 2935.jpg\n",
      "│       │   ├── 2936.jpg\n",
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      "│       │   ├── 293.jpg\n",
      "│       │   ├── 2942.jpg\n",
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      "│       │   ├── 2944.jpg\n",
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      "│       │   ├── 2947.jpg\n",
      "│       │   ├── 294.jpg\n",
      "│       │   ├── 2950.jpg\n",
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      "│       │   ├── 2958.jpg\n",
      "│       │   ├── 2961.jpg\n",
      "│       │   ├── 2962.jpg\n",
      "│       │   ├── 2963.jpg\n",
      "│       │   ├── 2964.jpg\n",
      "│       │   ├── 2967.jpg\n",
      "│       │   ├── 2968.jpg\n",
      "│       │   ├── 2973.jpg\n",
      "│       │   ├── 2974.jpg\n",
      "│       │   ├── 2976.jpg\n",
      "│       │   ├── 2978.jpg\n",
      "│       │   ├── 297.jpg\n",
      "│       │   ├── 2980.jpg\n",
      "│       │   ├── 2981.jpg\n",
      "│       │   ├── 2984.jpg\n",
      "│       │   ├── 2987.jpg\n",
      "│       │   ├── 2990.jpg\n",
      "│       │   ├── 2991.jpg\n",
      "│       │   ├── 2997.jpg\n",
      "│       │   ├── 29.jpg\n",
      "│       │   ├── 3001.jpg\n",
      "│       │   ├── 3003.jpg\n",
      "│       │   ├── 3004.jpg\n",
      "│       │   ├── 3007.jpg\n",
      "│       │   ├── 3009.jpg\n",
      "│       │   ├── 300.jpg\n",
      "│       │   ├── 3010.jpg\n",
      "│       │   ├── 3012.jpg\n",
      "│       │   ├── 3015.jpg\n",
      "│       │   ├── 3018.jpg\n",
      "│       │   ├── 3021.jpg\n",
      "│       │   ├── 3025.jpg\n",
      "│       │   ├── 3027.jpg\n",
      "│       │   ├── 3028.jpg\n",
      "│       │   ├── 302.jpg\n",
      "│       │   ├── 3032.jpg\n",
      "│       │   ├── 3040.jpg\n",
      "│       │   ├── 3041.jpg\n",
      "│       │   ├── 3048.jpg\n",
      "│       │   ├── 3049.jpg\n",
      "│       │   ├── 304.jpg\n",
      "│       │   ├── 3050.jpg\n",
      "│       │   ├── 3051.jpg\n",
      "│       │   ├── 3056.jpg\n",
      "│       │   ├── 3057.jpg\n",
      "│       │   ├── 3059.jpg\n",
      "│       │   ├── 305.jpg\n",
      "│       │   ├── 3067.jpg\n",
      "│       │   ├── 3068.jpg\n",
      "│       │   ├── 3069.jpg\n",
      "│       │   ├── 3070.jpg\n",
      "│       │   ├── 3072.jpg\n",
      "│       │   ├── 3073.jpg\n",
      "│       │   ├── 3074.jpg\n",
      "│       │   ├── 3078.jpg\n",
      "│       │   ├── 307.jpg\n",
      "│       │   ├── 3081.jpg\n",
      "│       │   ├── 3082.jpg\n",
      "│       │   ├── 3084.jpg\n",
      "│       │   ├── 3086.jpg\n",
      "│       │   ├── 3087.jpg\n",
      "│       │   ├── 3089.jpg\n",
      "│       │   ├── 308.jpg\n",
      "│       │   ├── 3092.jpg\n",
      "│       │   ├── 3097.jpg\n",
      "│       │   ├── 3099.jpg\n",
      "│       │   ├── 309.jpg\n",
      "│       │   ├── 3100.jpg\n",
      "│       │   ├── 3101.jpg\n",
      "│       │   ├── 3102.jpg\n",
      "│       │   ├── 3103.jpg\n",
      "│       │   ├── 3104.jpg\n",
      "│       │   ├── 3107.jpg\n",
      "│       │   ├── 3111.jpg\n",
      "│       │   ├── 3112.jpg\n",
      "│       │   ├── 3115.jpg\n",
      "│       │   ├── 3117.jpg\n",
      "│       │   ├── 3119.jpg\n",
      "│       │   ├── 3123.jpg\n",
      "│       │   ├── 3125.jpg\n",
      "│       │   ├── 312.jpg\n",
      "│       │   ├── 3135.jpg\n",
      "│       │   ├── 3136.jpg\n",
      "│       │   ├── 313.jpg\n",
      "│       │   ├── 3141.jpg\n",
      "│       │   ├── 3145.jpg\n",
      "│       │   ├── 3146.jpg\n",
      "│       │   ├── 3147.jpg\n",
      "│       │   ├── 3148.jpg\n",
      "│       │   ├── 314.jpg\n",
      "│       │   ├── 3150.jpg\n",
      "│       │   ├── 3152.jpg\n",
      "│       │   ├── 3153.jpg\n",
      "│       │   ├── 3155.jpg\n",
      "│       │   ├── 3157.jpg\n",
      "│       │   ├── 3158.jpg\n",
      "│       │   ├── 3159.jpg\n",
      "│       │   ├── 315.jpg\n",
      "│       │   ├── 3160.jpg\n",
      "│       │   ├── 3161.jpg\n",
      "│       │   ├── 3162.jpg\n",
      "│       │   ├── 3163.jpg\n",
      "│       │   ├── 3165.jpg\n",
      "│       │   ├── 3168.jpg\n",
      "│       │   ├── 3171.jpg\n",
      "│       │   ├── 3172.jpg\n",
      "│       │   ├── 3173.jpg\n",
      "│       │   ├── 317.jpg\n",
      "│       │   ├── 3180.jpg\n",
      "│       │   ├── 3181.jpg\n",
      "│       │   ├── 3182.jpg\n",
      "│       │   ├── 3183.jpg\n",
      "│       │   ├── 3184.jpg\n",
      "│       │   ├── 3186.jpg\n",
      "│       │   ├── 3189.jpg\n",
      "│       │   ├── 318.jpg\n",
      "│       │   ├── 3198.jpg\n",
      "│       │   ├── 3199.jpg\n",
      "│       │   ├── 319.jpg\n",
      "│       │   ├── 3201.jpg\n",
      "│       │   ├── 3205.jpg\n",
      "│       │   ├── 3206.jpg\n",
      "│       │   ├── 3207.jpg\n",
      "│       │   ├── 320.jpg\n",
      "│       │   ├── 3211.jpg\n",
      "│       │   ├── 3216.jpg\n",
      "│       │   ├── 3219.jpg\n",
      "│       │   ├── 321.jpg\n",
      "│       │   ├── 3221.jpg\n",
      "│       │   ├── 3237.jpg\n",
      "│       │   ├── 3240.jpg\n",
      "│       │   ├── 3241.jpg\n",
      "│       │   ├── 3244.jpg\n",
      "│       │   ├── 3246.jpg\n",
      "│       │   ├── 3247.jpg\n",
      "│       │   ├── 3250.jpg\n",
      "│       │   ├── 3255.jpg\n",
      "│       │   ├── 3261.jpg\n",
      "│       │   ├── 3264.jpg\n",
      "│       │   ├── 3265.jpg\n",
      "│       │   ├── 3273.jpg\n",
      "│       │   ├── 3278.jpg\n",
      "│       │   ├── 3282.jpg\n",
      "│       │   ├── 3283.jpg\n",
      "│       │   ├── 3285.jpg\n",
      "│       │   ├── 3286.jpg\n",
      "│       │   ├── 3287.jpg\n",
      "│       │   ├── 3289.jpg\n",
      "│       │   ├── 328.jpg\n",
      "│       │   ├── 3291.jpg\n",
      "│       │   ├── 3295.jpg\n",
      "│       │   ├── 32.jpg\n",
      "│       │   ├── 3302.jpg\n",
      "│       │   ├── 3303.jpg\n",
      "│       │   ├── 3304.jpg\n",
      "│       │   ├── 3305.jpg\n",
      "│       │   ├── 3306.jpg\n",
      "│       │   ├── 3308.jpg\n",
      "│       │   ├── 3309.jpg\n",
      "│       │   ├── 3311.jpg\n",
      "│       │   ├── 3312.jpg\n",
      "│       │   ├── 3314.jpg\n",
      "│       │   ├── 3315.jpg\n",
      "│       │   ├── 3319.jpg\n",
      "│       │   ├── 3322.jpg\n",
      "│       │   ├── 3326.jpg\n",
      "│       │   ├── 3327.jpg\n",
      "│       │   ├── 3328.jpg\n",
      "│       │   ├── 3330.jpg\n",
      "│       │   ├── 3334.jpg\n",
      "│       │   ├── 3336.jpg\n",
      "│       │   ├── 3337.jpg\n",
      "│       │   ├── 3339.jpg\n",
      "│       │   ├── 333.jpg\n",
      "│       │   ├── 3340.jpg\n",
      "│       │   ├── 3341.jpg\n",
      "│       │   ├── 3346.jpg\n",
      "│       │   ├── 3347.jpg\n",
      "│       │   ├── 3349.jpg\n",
      "│       │   ├── 3351.jpg\n",
      "│       │   ├── 3356.jpg\n",
      "│       │   ├── 3357.jpg\n",
      "│       │   ├── 3359.jpg\n",
      "│       │   ├── 3360.jpg\n",
      "│       │   ├── 3361.jpg\n",
      "│       │   ├── 3362.jpg\n",
      "│       │   ├── 3364.jpg\n",
      "│       │   ├── 3365.jpg\n",
      "│       │   ├── 3366.jpg\n",
      "│       │   ├── 3368.jpg\n",
      "│       │   ├── 3369.jpg\n",
      "│       │   ├── 336.jpg\n",
      "│       │   ├── 3370.jpg\n",
      "│       │   ├── 3377.jpg\n",
      "│       │   ├── 337.jpg\n",
      "│       │   ├── 3380.jpg\n",
      "│       │   ├── 3382.jpg\n",
      "│       │   ├── 3383.jpg\n",
      "│       │   ├── 3385.jpg\n",
      "│       │   ├── 3386.jpg\n",
      "│       │   ├── 3387.jpg\n",
      "│       │   ├── 3388.jpg\n",
      "│       │   ├── 3389.jpg\n",
      "│       │   ├── 3391.jpg\n",
      "│       │   ├── 3395.jpg\n",
      "│       │   ├── 3397.jpg\n",
      "│       │   ├── 3398.jpg\n",
      "│       │   ├── 339.jpg\n",
      "│       │   ├── 33.jpg\n",
      "│       │   ├── 3403.jpg\n",
      "│       │   ├── 3406.jpg\n",
      "│       │   ├── 3409.jpg\n",
      "│       │   ├── 340.jpg\n",
      "│       │   ├── 3411.jpg\n",
      "│       │   ├── 3413.jpg\n",
      "│       │   ├── 3415.jpg\n",
      "│       │   ├── 3418.jpg\n",
      "│       │   ├── 3419.jpg\n",
      "│       │   ├── 3422.jpg\n",
      "│       │   ├── 3423.jpg\n",
      "│       │   ├── 3424.jpg\n",
      "│       │   ├── 3427.jpg\n",
      "│       │   ├── 3428.jpg\n",
      "│       │   ├── 3430.jpg\n",
      "│       │   ├── 3437.jpg\n",
      "│       │   ├── 3439.jpg\n",
      "│       │   ├── 3444.jpg\n",
      "│       │   ├── 3446.jpg\n",
      "│       │   ├── 3448.jpg\n",
      "│       │   ├── 3449.jpg\n",
      "│       │   ├── 344.jpg\n",
      "│       │   ├── 3450.jpg\n",
      "│       │   ├── 3454.jpg\n",
      "│       │   ├── 3455.jpg\n",
      "│       │   ├── 3456.jpg\n",
      "│       │   ├── 3457.jpg\n",
      "│       │   ├── 3459.jpg\n",
      "│       │   ├── 345.jpg\n",
      "│       │   ├── 3461.jpg\n",
      "│       │   ├── 3462.jpg\n",
      "│       │   ├── 3467.jpg\n",
      "│       │   ├── 3469.jpg\n",
      "│       │   ├── 3471.jpg\n",
      "│       │   ├── 3472.jpg\n",
      "│       │   ├── 3475.jpg\n",
      "│       │   ├── 3476.jpg\n",
      "│       │   ├── 3477.jpg\n",
      "│       │   ├── 3478.jpg\n",
      "│       │   ├── 3479.jpg\n",
      "│       │   ├── 3481.jpg\n",
      "│       │   ├── 3486.jpg\n",
      "│       │   ├── 3487.jpg\n",
      "│       │   ├── 3488.jpg\n",
      "│       │   ├── 348.jpg\n",
      "│       │   ├── 3490.jpg\n",
      "│       │   ├── 3491.jpg\n",
      "│       │   ├── 3492.jpg\n",
      "│       │   ├── 3494.jpg\n",
      "│       │   ├── 3500.jpg\n",
      "│       │   ├── 3507.jpg\n",
      "│       │   ├── 350.jpg\n",
      "│       │   ├── 3513.jpg\n",
      "│       │   ├── 3515.jpg\n",
      "│       │   ├── 3516.jpg\n",
      "│       │   ├── 3519.jpg\n",
      "│       │   ├── 351.jpg\n",
      "│       │   ├── 3520.jpg\n",
      "│       │   ├── 3522.jpg\n",
      "│       │   ├── 3523.jpg\n",
      "│       │   ├── 3524.jpg\n",
      "│       │   ├── 3527.jpg\n",
      "│       │   ├── 3528.jpg\n",
      "│       │   ├── 3530.jpg\n",
      "│       │   ├── 3531.jpg\n",
      "│       │   ├── 3532.jpg\n",
      "│       │   ├── 3533.jpg\n",
      "│       │   ├── 3535.jpg\n",
      "│       │   ├── 3536.jpg\n",
      "│       │   ├── 3537.jpg\n",
      "│       │   ├── 3539.jpg\n",
      "│       │   ├── 3541.jpg\n",
      "│       │   ├── 3546.jpg\n",
      "│       │   ├── 3547.jpg\n",
      "│       │   ├── 3549.jpg\n",
      "│       │   ├── 354.jpg\n",
      "│       │   ├── 3550.jpg\n",
      "│       │   ├── 3551.jpg\n",
      "│       │   ├── 3552.jpg\n",
      "│       │   ├── 3553.jpg\n",
      "│       │   ├── 3555.jpg\n",
      "│       │   ├── 3559.jpg\n",
      "│       │   ├── 355.jpg\n",
      "│       │   ├── 3561.jpg\n",
      "│       │   ├── 3562.jpg\n",
      "│       │   ├── 3564.jpg\n",
      "│       │   ├── 3568.jpg\n",
      "│       │   ├── 3569.jpg\n",
      "│       │   ├── 3570.jpg\n",
      "│       │   ├── 3573.jpg\n",
      "│       │   ├── 3574.jpg\n",
      "│       │   ├── 3576.jpg\n",
      "│       │   ├── 3577.jpg\n",
      "│       │   ├── 3578.jpg\n",
      "│       │   ├── 3580.jpg\n",
      "│       │   ├── 3581.jpg\n",
      "│       │   ├── 3583.jpg\n",
      "│       │   ├── 3585.jpg\n",
      "│       │   ├── 3586.jpg\n",
      "│       │   ├── 358.jpg\n",
      "│       │   ├── 3593.jpg\n",
      "│       │   ├── 3595.jpg\n",
      "│       │   ├── 3597.jpg\n",
      "│       │   ├── 3598.jpg\n",
      "│       │   ├── 359.jpg\n",
      "│       │   ├── 35.jpg\n",
      "│       │   ├── 3600.jpg\n",
      "│       │   ├── 3601.jpg\n",
      "│       │   ├── 3605.jpg\n",
      "│       │   ├── 3607.jpg\n",
      "│       │   ├── 3608.jpg\n",
      "│       │   ├── 360.jpg\n",
      "│       │   ├── 3612.jpg\n",
      "│       │   ├── 3617.jpg\n",
      "│       │   ├── 3618.jpg\n",
      "│       │   ├── 3619.jpg\n",
      "│       │   ├── 3622.jpg\n",
      "│       │   ├── 3626.jpg\n",
      "│       │   ├── 3628.jpg\n",
      "│       │   ├── 3631.jpg\n",
      "│       │   ├── 3638.jpg\n",
      "│       │   ├── 3640.jpg\n",
      "│       │   ├── 3641.jpg\n",
      "│       │   ├── 3643.jpg\n",
      "│       │   ├── 3646.jpg\n",
      "│       │   ├── 364.jpg\n",
      "│       │   ├── 3650.jpg\n",
      "│       │   ├── 3652.jpg\n",
      "│       │   ├── 3656.jpg\n",
      "│       │   ├── 3658.jpg\n",
      "│       │   ├── 3659.jpg\n",
      "│       │   ├── 3661.jpg\n",
      "│       │   ├── 3662.jpg\n",
      "│       │   ├── 3664.jpg\n",
      "│       │   ├── 3668.jpg\n",
      "│       │   ├── 3671.jpg\n",
      "│       │   ├── 3672.jpg\n",
      "│       │   ├── 3673.jpg\n",
      "│       │   ├── 3676.jpg\n",
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      "│       │   ├── 3679.jpg\n",
      "│       │   ├── 3684.jpg\n",
      "│       │   ├── 3686.jpg\n",
      "│       │   ├── 3687.jpg\n",
      "│       │   ├── 3689.jpg\n",
      "│       │   ├── 3690.jpg\n",
      "│       │   ├── 3692.jpg\n",
      "│       │   ├── 3694.jpg\n",
      "│       │   ├── 3699.jpg\n",
      "│       │   ├── 3704.jpg\n",
      "│       │   ├── 3706.jpg\n",
      "│       │   ├── 3708.jpg\n",
      "│       │   ├── 3710.jpg\n",
      "│       │   ├── 3714.jpg\n",
      "│       │   ├── 3717.jpg\n",
      "│       │   ├── 3718.jpg\n",
      "│       │   ├── 3720.jpg\n",
      "│       │   ├── 3721.jpg\n",
      "│       │   ├── 3724.jpg\n",
      "│       │   ├── 3727.jpg\n",
      "│       │   ├── 3729.jpg\n",
      "│       │   ├── 3730.jpg\n",
      "│       │   ├── 3732.jpg\n",
      "│       │   ├── 3734.jpg\n",
      "│       │   ├── 3735.jpg\n",
      "│       │   ├── 3737.jpg\n",
      "│       │   ├── 3739.jpg\n",
      "│       │   ├── 3742.jpg\n",
      "│       │   ├── 3745.jpg\n",
      "│       │   ├── 3747.jpg\n",
      "│       │   ├── 3748.jpg\n",
      "│       │   ├── 374.jpg\n",
      "│       │   ├── 3751.jpg\n",
      "│       │   ├── 3752.jpg\n",
      "│       │   ├── 3753.jpg\n",
      "│       │   ├── 3758.jpg\n",
      "│       │   ├── 3761.jpg\n",
      "│       │   ├── 3763.jpg\n",
      "│       │   ├── 3765.jpg\n",
      "│       │   ├── 3767.jpg\n",
      "│       │   ├── 3768.jpg\n",
      "│       │   ├── 3769.jpg\n",
      "│       │   ├── 3770.jpg\n",
      "│       │   ├── 3771.jpg\n",
      "│       │   ├── 3775.jpg\n",
      "│       │   ├── 3776.jpg\n",
      "│       │   ├── 3777.jpg\n",
      "│       │   ├── 3778.jpg\n",
      "│       │   ├── 3781.jpg\n",
      "│       │   ├── 3782.jpg\n",
      "│       │   ├── 3783.jpg\n",
      "│       │   ├── 3785.jpg\n",
      "│       │   ├── 3789.jpg\n",
      "│       │   ├── 378.jpg\n",
      "│       │   ├── 3797.jpg\n",
      "│       │   ├── 37.jpg\n",
      "│       │   ├── 3802.jpg\n",
      "│       │   ├── 3807.jpg\n",
      "│       │   ├── 3808.jpg\n",
      "│       │   ├── 3809.jpg\n",
      "│       │   ├── 3813.jpg\n",
      "│       │   ├── 3814.jpg\n",
      "│       │   ├── 3815.jpg\n",
      "│       │   ├── 3816.jpg\n",
      "│       │   ├── 3817.jpg\n",
      "│       │   ├── 3818.jpg\n",
      "│       │   ├── 3825.jpg\n",
      "│       │   ├── 3826.jpg\n",
      "│       │   ├── 3827.jpg\n",
      "│       │   ├── 3828.jpg\n",
      "│       │   ├── 382.jpg\n",
      "│       │   ├── 3830.jpg\n",
      "│       │   ├── 3837.jpg\n",
      "│       │   ├── 3845.jpg\n",
      "│       │   ├── 3846.jpg\n",
      "│       │   ├── 3847.jpg\n",
      "│       │   ├── 3848.jpg\n",
      "│       │   ├── 3849.jpg\n",
      "│       │   ├── 3850.jpg\n",
      "│       │   ├── 3851.jpg\n",
      "│       │   ├── 3852.jpg\n",
      "│       │   ├── 3855.jpg\n",
      "│       │   ├── 3856.jpg\n",
      "│       │   ├── 3857.jpg\n",
      "│       │   ├── 3858.jpg\n",
      "│       │   ├── 3859.jpg\n",
      "│       │   ├── 3860.jpg\n",
      "│       │   ├── 3861.jpg\n",
      "│       │   ├── 3864.jpg\n",
      "│       │   ├── 3866.jpg\n",
      "│       │   ├── 3867.jpg\n",
      "│       │   ├── 3869.jpg\n",
      "│       │   ├── 3874.jpg\n",
      "│       │   ├── 3875.jpg\n",
      "│       │   ├── 3877.jpg\n",
      "│       │   ├── 3878.jpg\n",
      "│       │   ├── 3879.jpg\n",
      "│       │   ├── 3881.jpg\n",
      "│       │   ├── 3888.jpg\n",
      "│       │   ├── 388.jpg\n",
      "│       │   ├── 3891.jpg\n",
      "│       │   ├── 3892.jpg\n",
      "│       │   ├── 3893.jpg\n",
      "│       │   ├── 3895.jpg\n",
      "│       │   ├── 3896.jpg\n",
      "│       │   ├── 389.jpg\n",
      "│       │   ├── 3901.jpg\n",
      "│       │   ├── 3904.jpg\n",
      "│       │   ├── 3908.jpg\n",
      "│       │   ├── 390.jpg\n",
      "│       │   ├── 3911.jpg\n",
      "│       │   ├── 3912.jpg\n",
      "│       │   ├── 3914.jpg\n",
      "│       │   ├── 3915.jpg\n",
      "│       │   ├── 3922.jpg\n",
      "│       │   ├── 3923.jpg\n",
      "│       │   ├── 3925.jpg\n",
      "│       │   ├── 3926.jpg\n",
      "│       │   ├── 3928.jpg\n",
      "│       │   ├── 3929.jpg\n",
      "│       │   ├── 3930.jpg\n",
      "│       │   ├── 3933.jpg\n",
      "│       │   ├── 3935.jpg\n",
      "│       │   ├── 3936.jpg\n",
      "│       │   ├── 3937.jpg\n",
      "│       │   ├── 3938.jpg\n",
      "│       │   ├── 393.jpg\n",
      "│       │   ├── 3942.jpg\n",
      "│       │   ├── 3943.jpg\n",
      "│       │   ├── 3944.jpg\n",
      "│       │   ├── 3945.jpg\n",
      "│       │   ├── 3948.jpg\n",
      "│       │   ├── 394.jpg\n",
      "│       │   ├── 3950.jpg\n",
      "│       │   ├── 3951.jpg\n",
      "│       │   ├── 3952.jpg\n",
      "│       │   ├── 3953.jpg\n",
      "│       │   ├── 3954.jpg\n",
      "│       │   ├── 3958.jpg\n",
      "│       │   ├── 3959.jpg\n",
      "│       │   ├── 3960.jpg\n",
      "│       │   ├── 3961.jpg\n",
      "│       │   ├── 3963.jpg\n",
      "│       │   ├── 3967.jpg\n",
      "│       │   ├── 3968.jpg\n",
      "│       │   ├── 396.jpg\n",
      "│       │   ├── 3971.jpg\n",
      "│       │   ├── 3975.jpg\n",
      "│       │   ├── 3976.jpg\n",
      "│       │   ├── 3978.jpg\n",
      "│       │   ├── 3979.jpg\n",
      "│       │   ├── 397.jpg\n",
      "│       │   ├── 3980.jpg\n",
      "│       │   ├── 3982.jpg\n",
      "│       │   ├── 3983.jpg\n",
      "│       │   ├── 3987.jpg\n",
      "│       │   ├── 3988.jpg\n",
      "│       │   ├── 3990.jpg\n",
      "│       │   ├── 3991.jpg\n",
      "│       │   ├── 3994.jpg\n",
      "│       │   ├── 3995.jpg\n",
      "│       │   ├── 3998.jpg\n",
      "│       │   ├── 3.jpg\n",
      "│       │   ├── 4001.jpg\n",
      "│       │   ├── 4002.jpg\n",
      "│       │   ├── 4003.jpg\n",
      "│       │   ├── 4005.jpg\n",
      "│       │   ├── 4009.jpg\n",
      "│       │   ├── 400.jpg\n",
      "│       │   ├── 4011.jpg\n",
      "│       │   ├── 4012.jpg\n",
      "│       │   ├── 4013.jpg\n",
      "│       │   ├── 4017.jpg\n",
      "│       │   ├── 401.jpg\n",
      "│       │   ├── 4021.jpg\n",
      "│       │   ├── 4023.jpg\n",
      "│       │   ├── 4026.jpg\n",
      "│       │   ├── 4029.jpg\n",
      "│       │   ├── 4032.jpg\n",
      "│       │   ├── 4033.jpg\n",
      "│       │   ├── 4034.jpg\n",
      "│       │   ├── 4035.jpg\n",
      "│       │   ├── 4037.jpg\n",
      "│       │   ├── 4039.jpg\n",
      "│       │   ├── 4040.jpg\n",
      "│       │   ├── 4043.jpg\n",
      "│       │   ├── 4044.jpg\n",
      "│       │   ├── 4045.jpg\n",
      "│       │   ├── 4046.jpg\n",
      "│       │   ├── 4047.jpg\n",
      "│       │   ├── 4048.jpg\n",
      "│       │   ├── 404.jpg\n",
      "│       │   ├── 4052.jpg\n",
      "│       │   ├── 4054.jpg\n",
      "│       │   ├── 4056.jpg\n",
      "│       │   ├── 4057.jpg\n",
      "│       │   ├── 405.jpg\n",
      "│       │   ├── 4061.jpg\n",
      "│       │   ├── 4065.jpg\n",
      "│       │   ├── 4068.jpg\n",
      "│       │   ├── 406.jpg\n",
      "│       │   ├── 4070.jpg\n",
      "│       │   ├── 4074.jpg\n",
      "│       │   ├── 4076.jpg\n",
      "│       │   ├── 4077.jpg\n",
      "│       │   ├── 4078.jpg\n",
      "│       │   ├── 4079.jpg\n",
      "│       │   ├── 4081.jpg\n",
      "│       │   ├── 4082.jpg\n",
      "│       │   ├── 4084.jpg\n",
      "│       │   ├── 4085.jpg\n",
      "│       │   ├── 4087.jpg\n",
      "│       │   ├── 4088.jpg\n",
      "│       │   ├── 408.jpg\n",
      "│       │   ├── 4092.jpg\n",
      "│       │   ├── 4093.jpg\n",
      "│       │   ├── 4094.jpg\n",
      "│       │   ├── 4097.jpg\n",
      "│       │   ├── 4098.jpg\n",
      "│       │   ├── 40.jpg\n",
      "│       │   ├── 4101.jpg\n",
      "│       │   ├── 4103.jpg\n",
      "│       │   ├── 4104.jpg\n",
      "│       │   ├── 4105.jpg\n",
      "│       │   ├── 4106.jpg\n",
      "│       │   ├── 4107.jpg\n",
      "│       │   ├── 4108.jpg\n",
      "│       │   ├── 4115.jpg\n",
      "│       │   ├── 4116.jpg\n",
      "│       │   ├── 4118.jpg\n",
      "│       │   ├── 4120.jpg\n",
      "│       │   ├── 4123.jpg\n",
      "│       │   ├── 4125.jpg\n",
      "│       │   ├── 4126.jpg\n",
      "│       │   ├── 4129.jpg\n",
      "│       │   ├── 4131.jpg\n",
      "│       │   ├── 4132.jpg\n",
      "│       │   ├── 4134.jpg\n",
      "│       │   ├── 4137.jpg\n",
      "│       │   ├── 4140.jpg\n",
      "│       │   ├── 4142.jpg\n",
      "│       │   ├── 4145.jpg\n",
      "│       │   ├── 4148.jpg\n",
      "│       │   ├── 4149.jpg\n",
      "│       │   ├── 414.jpg\n",
      "│       │   ├── 4151.jpg\n",
      "│       │   ├── 4155.jpg\n",
      "│       │   ├── 4156.jpg\n",
      "│       │   ├── 4157.jpg\n",
      "│       │   ├── 4158.jpg\n",
      "│       │   ├── 415.jpg\n",
      "│       │   ├── 4161.jpg\n",
      "│       │   ├── 4162.jpg\n",
      "│       │   ├── 4165.jpg\n",
      "│       │   ├── 4167.jpg\n",
      "│       │   ├── 4168.jpg\n",
      "│       │   ├── 4169.jpg\n",
      "│       │   ├── 4170.jpg\n",
      "│       │   ├── 4174.jpg\n",
      "│       │   ├── 4176.jpg\n",
      "│       │   ├── 4177.jpg\n",
      "│       │   ├── 4178.jpg\n",
      "│       │   ├── 417.jpg\n",
      "│       │   ├── 4183.jpg\n",
      "│       │   ├── 4184.jpg\n",
      "│       │   ├── 4185.jpg\n",
      "│       │   ├── 4187.jpg\n",
      "│       │   ├── 4189.jpg\n",
      "│       │   ├── 418.jpg\n",
      "│       │   ├── 4190.jpg\n",
      "│       │   ├── 4191.jpg\n",
      "│       │   ├── 4193.jpg\n",
      "│       │   ├── 4194.jpg\n",
      "│       │   ├── 4195.jpg\n",
      "│       │   ├── 4196.jpg\n",
      "│       │   ├── 4197.jpg\n",
      "│       │   ├── 4198.jpg\n",
      "│       │   ├── 4199.jpg\n",
      "│       │   ├── 4200.jpg\n",
      "│       │   ├── 4204.jpg\n",
      "│       │   ├── 4209.jpg\n",
      "│       │   ├── 420.jpg\n",
      "│       │   ├── 4210.jpg\n",
      "│       │   ├── 4211.jpg\n",
      "│       │   ├── 4213.jpg\n",
      "│       │   ├── 4214.jpg\n",
      "│       │   ├── 4216.jpg\n",
      "│       │   ├── 4218.jpg\n",
      "│       │   ├── 4220.jpg\n",
      "│       │   ├── 4223.jpg\n",
      "│       │   ├── 4225.jpg\n",
      "│       │   ├── 4226.jpg\n",
      "│       │   ├── 4227.jpg\n",
      "│       │   ├── 4231.jpg\n",
      "│       │   ├── 4234.jpg\n",
      "│       │   ├── 4236.jpg\n",
      "│       │   ├── 4237.jpg\n",
      "│       │   ├── 423.jpg\n",
      "│       │   ├── 4240.jpg\n",
      "│       │   ├── 4243.jpg\n",
      "│       │   ├── 4245.jpg\n",
      "│       │   ├── 4247.jpg\n",
      "│       │   ├── 4248.jpg\n",
      "│       │   ├── 4249.jpg\n",
      "│       │   ├── 4250.jpg\n",
      "│       │   ├── 4253.jpg\n",
      "│       │   ├── 4258.jpg\n",
      "│       │   ├── 4259.jpg\n",
      "│       │   ├── 4264.jpg\n",
      "│       │   ├── 4271.jpg\n",
      "│       │   ├── 4273.jpg\n",
      "│       │   ├── 4274.jpg\n",
      "│       │   ├── 4275.jpg\n",
      "│       │   ├── 4278.jpg\n",
      "│       │   ├── 4279.jpg\n",
      "│       │   ├── 4281.jpg\n",
      "│       │   ├── 4282.jpg\n",
      "│       │   ├── 4285.jpg\n",
      "│       │   ├── 4286.jpg\n",
      "│       │   ├── 4287.jpg\n",
      "│       │   ├── 4289.jpg\n",
      "│       │   ├── 4292.jpg\n",
      "│       │   ├── 4294.jpg\n",
      "│       │   ├── 4298.jpg\n",
      "│       │   ├── 4299.jpg\n",
      "│       │   ├── 429.jpg\n",
      "│       │   ├── 42.jpg\n",
      "│       │   ├── 4301.jpg\n",
      "│       │   ├── 4304.jpg\n",
      "│       │   ├── 4305.jpg\n",
      "│       │   ├── 4307.jpg\n",
      "│       │   ├── 4308.jpg\n",
      "│       │   ├── 430.jpg\n",
      "│       │   ├── 4310.jpg\n",
      "│       │   ├── 4311.jpg\n",
      "│       │   ├── 4313.jpg\n",
      "│       │   ├── 4317.jpg\n",
      "│       │   ├── 4320.jpg\n",
      "│       │   ├── 4321.jpg\n",
      "│       │   ├── 4326.jpg\n",
      "│       │   ├── 4328.jpg\n",
      "│       │   ├── 432.jpg\n",
      "│       │   ├── 4333.jpg\n",
      "│       │   ├── 4334.jpg\n",
      "│       │   ├── 4335.jpg\n",
      "│       │   ├── 4336.jpg\n",
      "│       │   ├── 4338.jpg\n",
      "│       │   ├── 433.jpg\n",
      "│       │   ├── 4340.jpg\n",
      "│       │   ├── 4343.jpg\n",
      "│       │   ├── 4344.jpg\n",
      "│       │   ├── 4345.jpg\n",
      "│       │   ├── 4346.jpg\n",
      "│       │   ├── 4349.jpg\n",
      "│       │   ├── 4350.jpg\n",
      "│       │   ├── 4351.jpg\n",
      "│       │   ├── 4352.jpg\n",
      "│       │   ├── 4353.jpg\n",
      "│       │   ├── 4355.jpg\n",
      "│       │   ├── 4356.jpg\n",
      "│       │   ├── 4358.jpg\n",
      "│       │   ├── 4360.jpg\n",
      "│       │   ├── 4361.jpg\n",
      "│       │   ├── 4362.jpg\n",
      "│       │   ├── 436.jpg\n",
      "│       │   ├── 4372.jpg\n",
      "│       │   ├── 4375.jpg\n",
      "│       │   ├── 4377.jpg\n",
      "│       │   ├── 4379.jpg\n",
      "│       │   ├── 4380.jpg\n",
      "│       │   ├── 4386.jpg\n",
      "│       │   ├── 438.jpg\n",
      "│       │   ├── 4392.jpg\n",
      "│       │   ├── 4397.jpg\n",
      "│       │   ├── 4398.jpg\n",
      "│       │   ├── 4400.jpg\n",
      "│       │   ├── 4403.jpg\n",
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      "│       │   ├── 4406.jpg\n",
      "│       │   ├── 4409.jpg\n",
      "│       │   ├── 4413.jpg\n",
      "│       │   ├── 4414.jpg\n",
      "│       │   ├── 4416.jpg\n",
      "│       │   ├── 4418.jpg\n",
      "│       │   ├── 441.jpg\n",
      "│       │   ├── 4420.jpg\n",
      "│       │   ├── 4421.jpg\n",
      "│       │   ├── 4422.jpg\n",
      "│       │   ├── 4423.jpg\n",
      "│       │   ├── 4424.jpg\n",
      "│       │   ├── 4426.jpg\n",
      "│       │   ├── 4427.jpg\n",
      "│       │   ├── 4429.jpg\n",
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      "│       │   ├── 4430.jpg\n",
      "│       │   ├── 4434.jpg\n",
      "│       │   ├── 4440.jpg\n",
      "│       │   ├── 4444.jpg\n",
      "│       │   ├── 4448.jpg\n",
      "│       │   ├── 4449.jpg\n",
      "│       │   ├── 444.jpg\n",
      "│       │   ├── 4450.jpg\n",
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      "│       │   ├── 4456.jpg\n",
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      "│       │   ├── 4459.jpg\n",
      "│       │   ├── 445.jpg\n",
      "│       │   ├── 4461.jpg\n",
      "│       │   ├── 4466.jpg\n",
      "│       │   ├── 4468.jpg\n",
      "│       │   ├── 4469.jpg\n",
      "│       │   ├── 4470.jpg\n",
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      "│       │   ├── 4475.jpg\n",
      "│       │   ├── 4478.jpg\n",
      "│       │   ├── 447.jpg\n",
      "│       │   ├── 4480.jpg\n",
      "│       │   ├── 4481.jpg\n",
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      "│       │   ├── 4485.jpg\n",
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      "│       │   ├── 448.jpg\n",
      "│       │   ├── 4490.jpg\n",
      "│       │   ├── 4491.jpg\n",
      "│       │   ├── 4498.jpg\n",
      "│       │   ├── 449.jpg\n",
      "│       │   ├── 44.jpg\n",
      "│       │   ├── 4500.jpg\n",
      "│       │   ├── 4505.jpg\n",
      "│       │   ├── 4506.jpg\n",
      "│       │   ├── 4507.jpg\n",
      "│       │   ├── 450.jpg\n",
      "│       │   ├── 4511.jpg\n",
      "│       │   ├── 4512.jpg\n",
      "│       │   ├── 4513.jpg\n",
      "│       │   ├── 4515.jpg\n",
      "│       │   ├── 451.jpg\n",
      "│       │   ├── 4520.jpg\n",
      "│       │   ├── 4522.jpg\n",
      "│       │   ├── 4526.jpg\n",
      "│       │   ├── 4528.jpg\n",
      "│       │   ├── 4529.jpg\n",
      "│       │   ├── 452.jpg\n",
      "│       │   ├── 4532.jpg\n",
      "│       │   ├── 4534.jpg\n",
      "│       │   ├── 4535.jpg\n",
      "│       │   ├── 4537.jpg\n",
      "│       │   ├── 4538.jpg\n",
      "│       │   ├── 4539.jpg\n",
      "│       │   ├── 453.jpg\n",
      "│       │   ├── 4540.jpg\n",
      "│       │   ├── 4546.jpg\n",
      "│       │   ├── 4547.jpg\n",
      "│       │   ├── 4548.jpg\n",
      "│       │   ├── 4550.jpg\n",
      "│       │   ├── 4551.jpg\n",
      "│       │   ├── 4554.jpg\n",
      "│       │   ├── 4556.jpg\n",
      "│       │   ├── 4558.jpg\n",
      "│       │   ├── 455.jpg\n",
      "│       │   ├── 4560.jpg\n",
      "│       │   ├── 4563.jpg\n",
      "│       │   ├── 4571.jpg\n",
      "│       │   ├── 4573.jpg\n",
      "│       │   ├── 4576.jpg\n",
      "│       │   ├── 4579.jpg\n",
      "│       │   ├── 457.jpg\n",
      "│       │   ├── 4582.jpg\n",
      "│       │   ├── 4583.jpg\n",
      "│       │   ├── 4585.jpg\n",
      "│       │   ├── 4586.jpg\n",
      "│       │   ├── 4587.jpg\n",
      "│       │   ├── 4588.jpg\n",
      "│       │   ├── 4589.jpg\n",
      "│       │   ├── 4591.jpg\n",
      "│       │   ├── 4592.jpg\n",
      "│       │   ├── 4595.jpg\n",
      "│       │   ├── 4596.jpg\n",
      "│       │   ├── 459.jpg\n",
      "│       │   ├── 45.jpg\n",
      "│       │   ├── 4601.jpg\n",
      "│       │   ├── 4603.jpg\n",
      "│       │   ├── 4605.jpg\n",
      "│       │   ├── 4607.jpg\n",
      "│       │   ├── 4609.jpg\n",
      "│       │   ├── 4612.jpg\n",
      "│       │   ├── 4618.jpg\n",
      "│       │   ├── 4620.jpg\n",
      "│       │   ├── 4625.jpg\n",
      "│       │   ├── 4630.jpg\n",
      "│       │   ├── 4632.jpg\n",
      "│       │   ├── 4634.jpg\n",
      "│       │   ├── 4637.jpg\n",
      "│       │   ├── 4639.jpg\n",
      "│       │   ├── 4640.jpg\n",
      "│       │   ├── 4642.jpg\n",
      "│       │   ├── 4643.jpg\n",
      "│       │   ├── 4644.jpg\n",
      "│       │   ├── 4645.jpg\n",
      "│       │   ├── 4646.jpg\n",
      "│       │   ├── 4647.jpg\n",
      "│       │   ├── 464.jpg\n",
      "│       │   ├── 4652.jpg\n",
      "│       │   ├── 4653.jpg\n",
      "│       │   ├── 4656.jpg\n",
      "│       │   ├── 4657.jpg\n",
      "│       │   ├── 4658.jpg\n",
      "│       │   ├── 4659.jpg\n",
      "│       │   ├── 465.jpg\n",
      "│       │   ├── 4663.jpg\n",
      "│       │   ├── 4665.jpg\n",
      "│       │   ├── 4666.jpg\n",
      "│       │   ├── 4668.jpg\n",
      "│       │   ├── 4672.jpg\n",
      "│       │   ├── 4674.jpg\n",
      "│       │   ├── 4675.jpg\n",
      "│       │   ├── 4677.jpg\n",
      "│       │   ├── 4678.jpg\n",
      "│       │   ├── 467.jpg\n",
      "│       │   ├── 4681.jpg\n",
      "│       │   ├── 4683.jpg\n",
      "│       │   ├── 4686.jpg\n",
      "│       │   ├── 4688.jpg\n",
      "│       │   ├── 4690.jpg\n",
      "│       │   ├── 4693.jpg\n",
      "│       │   ├── 4695.jpg\n",
      "│       │   ├── 4697.jpg\n",
      "│       │   ├── 4699.jpg\n",
      "│       │   ├── 46.jpg\n",
      "│       │   ├── 4702.jpg\n",
      "│       │   ├── 4703.jpg\n",
      "│       │   ├── 4705.jpg\n",
      "│       │   ├── 4710.jpg\n",
      "│       │   ├── 4712.jpg\n",
      "│       │   ├── 4714.jpg\n",
      "│       │   ├── 4717.jpg\n",
      "│       │   ├── 4718.jpg\n",
      "│       │   ├── 4719.jpg\n",
      "│       │   ├── 471.jpg\n",
      "│       │   ├── 4720.jpg\n",
      "│       │   ├── 4722.jpg\n",
      "│       │   ├── 4723.jpg\n",
      "│       │   ├── 4725.jpg\n",
      "│       │   ├── 4729.jpg\n",
      "│       │   ├── 4731.jpg\n",
      "│       │   ├── 4733.jpg\n",
      "│       │   ├── 4735.jpg\n",
      "│       │   ├── 4736.jpg\n",
      "│       │   ├── 4737.jpg\n",
      "│       │   ├── 4739.jpg\n",
      "│       │   ├── 473.jpg\n",
      "│       │   ├── 4740.jpg\n",
      "│       │   ├── 4742.jpg\n",
      "│       │   ├── 4745.jpg\n",
      "│       │   ├── 4746.jpg\n",
      "│       │   ├── 4747.jpg\n",
      "│       │   ├── 4750.jpg\n",
      "│       │   ├── 4751.jpg\n",
      "│       │   ├── 4752.jpg\n",
      "│       │   ├── 4753.jpg\n",
      "│       │   ├── 4754.jpg\n",
      "│       │   ├── 4756.jpg\n",
      "│       │   ├── 4762.jpg\n",
      "│       │   ├── 4764.jpg\n",
      "│       │   ├── 4767.jpg\n",
      "│       │   ├── 476.jpg\n",
      "│       │   ├── 4772.jpg\n",
      "│       │   ├── 4773.jpg\n",
      "│       │   ├── 4775.jpg\n",
      "│       │   ├── 4776.jpg\n",
      "│       │   ├── 4779.jpg\n",
      "│       │   ├── 4780.jpg\n",
      "│       │   ├── 4781.jpg\n",
      "│       │   ├── 4782.jpg\n",
      "│       │   ├── 4784.jpg\n",
      "│       │   ├── 4788.jpg\n",
      "│       │   ├── 478.jpg\n",
      "│       │   ├── 4792.jpg\n",
      "│       │   ├── 4793.jpg\n",
      "│       │   ├── 4795.jpg\n",
      "│       │   ├── 4799.jpg\n",
      "│       │   ├── 479.jpg\n",
      "│       │   ├── 47.jpg\n",
      "│       │   ├── 4800.jpg\n",
      "│       │   ├── 4801.jpg\n",
      "│       │   ├── 4802.jpg\n",
      "│       │   ├── 4805.jpg\n",
      "│       │   ├── 4806.jpg\n",
      "│       │   ├── 4807.jpg\n",
      "│       │   ├── 4808.jpg\n",
      "│       │   ├── 480.jpg\n",
      "│       │   ├── 4811.jpg\n",
      "│       │   ├── 4813.jpg\n",
      "│       │   ├── 4814.jpg\n",
      "│       │   ├── 4817.jpg\n",
      "│       │   ├── 4818.jpg\n",
      "│       │   ├── 4819.jpg\n",
      "│       │   ├── 4820.jpg\n",
      "│       │   ├── 4822.jpg\n",
      "│       │   ├── 4823.jpg\n",
      "│       │   ├── 4824.jpg\n",
      "│       │   ├── 4830.jpg\n",
      "│       │   ├── 4831.jpg\n",
      "│       │   ├── 4832.jpg\n",
      "│       │   ├── 4833.jpg\n",
      "│       │   ├── 4835.jpg\n",
      "│       │   ├── 4837.jpg\n",
      "│       │   ├── 4841.jpg\n",
      "│       │   ├── 4843.jpg\n",
      "│       │   ├── 4844.jpg\n",
      "│       │   ├── 4848.jpg\n",
      "│       │   ├── 4852.jpg\n",
      "│       │   ├── 4854.jpg\n",
      "│       │   ├── 4856.jpg\n",
      "│       │   ├── 4858.jpg\n",
      "│       │   ├── 4859.jpg\n",
      "│       │   ├── 485.jpg\n",
      "│       │   ├── 4860.jpg\n",
      "│       │   ├── 4866.jpg\n",
      "│       │   ├── 4868.jpg\n",
      "│       │   ├── 4869.jpg\n",
      "│       │   ├── 486.jpg\n",
      "│       │   ├── 4870.jpg\n",
      "│       │   ├── 4871.jpg\n",
      "│       │   ├── 4873.jpg\n",
      "│       │   ├── 4874.jpg\n",
      "│       │   ├── 4876.jpg\n",
      "│       │   ├── 4877.jpg\n",
      "│       │   ├── 4879.jpg\n",
      "│       │   ├── 4880.jpg\n",
      "│       │   ├── 4881.jpg\n",
      "│       │   ├── 4884.jpg\n",
      "│       │   ├── 4886.jpg\n",
      "│       │   ├── 488.jpg\n",
      "│       │   ├── 4893.jpg\n",
      "│       │   ├── 4894.jpg\n",
      "│       │   ├── 4896.jpg\n",
      "│       │   ├── 4899.jpg\n",
      "│       │   ├── 48.jpg\n",
      "│       │   ├── 4901.jpg\n",
      "│       │   ├── 4903.jpg\n",
      "│       │   ├── 4904.jpg\n",
      "│       │   ├── 4905.jpg\n",
      "│       │   ├── 4906.jpg\n",
      "│       │   ├── 4908.jpg\n",
      "│       │   ├── 490.jpg\n",
      "│       │   ├── 4910.jpg\n",
      "│       │   ├── 4913.jpg\n",
      "│       │   ├── 4915.jpg\n",
      "│       │   ├── 4916.jpg\n",
      "│       │   ├── 4918.jpg\n",
      "│       │   ├── 4922.jpg\n",
      "│       │   ├── 4926.jpg\n",
      "│       │   ├── 4928.jpg\n",
      "│       │   ├── 4929.jpg\n",
      "│       │   ├── 4930.jpg\n",
      "│       │   ├── 4931.jpg\n",
      "│       │   ├── 4932.jpg\n",
      "│       │   ├── 4938.jpg\n",
      "│       │   ├── 4939.jpg\n",
      "│       │   ├── 493.jpg\n",
      "│       │   ├── 4941.jpg\n",
      "│       │   ├── 4942.jpg\n",
      "│       │   ├── 4943.jpg\n",
      "│       │   ├── 4944.jpg\n",
      "│       │   ├── 4948.jpg\n",
      "│       │   ├── 494.jpg\n",
      "│       │   ├── 4952.jpg\n",
      "│       │   ├── 4953.jpg\n",
      "│       │   ├── 4954.jpg\n",
      "│       │   ├── 4957.jpg\n",
      "│       │   ├── 4959.jpg\n",
      "│       │   ├── 495.jpg\n",
      "│       │   ├── 4962.jpg\n",
      "│       │   ├── 4965.jpg\n",
      "│       │   ├── 4966.jpg\n",
      "│       │   ├── 4967.jpg\n",
      "│       │   ├── 4968.jpg\n",
      "│       │   ├── 4969.jpg\n",
      "│       │   ├── 4971.jpg\n",
      "│       │   ├── 4972.jpg\n",
      "│       │   ├── 4974.jpg\n",
      "│       │   ├── 4977.jpg\n",
      "│       │   ├── 4978.jpg\n",
      "│       │   ├── 497.jpg\n",
      "│       │   ├── 4983.jpg\n",
      "│       │   ├── 4984.jpg\n",
      "│       │   ├── 4988.jpg\n",
      "│       │   ├── 4989.jpg\n",
      "│       │   ├── 498.jpg\n",
      "│       │   ├── 4990.jpg\n",
      "│       │   ├── 4991.jpg\n",
      "│       │   ├── 4992.jpg\n",
      "│       │   ├── 4993.jpg\n",
      "│       │   ├── 4994.jpg\n",
      "│       │   ├── 4995.jpg\n",
      "│       │   ├── 4997.jpg\n",
      "│       │   ├── 4998.jpg\n",
      "│       │   ├── 4999.jpg\n",
      "│       │   ├── 4.jpg\n",
      "│       │   ├── 5003.jpg\n",
      "│       │   ├── 5004.jpg\n",
      "│       │   ├── 5005.jpg\n",
      "│       │   ├── 5010.jpg\n",
      "│       │   ├── 5016.jpg\n",
      "│       │   ├── 5017.jpg\n",
      "│       │   ├── 5019.jpg\n",
      "│       │   ├── 5022.jpg\n",
      "│       │   ├── 5023.jpg\n",
      "│       │   ├── 5027.jpg\n",
      "│       │   ├── 5028.jpg\n",
      "│       │   ├── 5030.jpg\n",
      "│       │   ├── 5032.jpg\n",
      "│       │   ├── 5035.jpg\n",
      "│       │   ├── 5037.jpg\n",
      "│       │   ├── 5039.jpg\n",
      "│       │   ├── 503.jpg\n",
      "│       │   ├── 5040.jpg\n",
      "│       │   ├── 5045.jpg\n",
      "│       │   ├── 5046.jpg\n",
      "│       │   ├── 5047.jpg\n",
      "│       │   ├── 5048.jpg\n",
      "│       │   ├── 5049.jpg\n",
      "│       │   ├── 5050.jpg\n",
      "│       │   ├── 5052.jpg\n",
      "│       │   ├── 5056.jpg\n",
      "│       │   ├── 5057.jpg\n",
      "│       │   ├── 5059.jpg\n",
      "│       │   ├── 5060.jpg\n",
      "│       │   ├── 5061.jpg\n",
      "│       │   ├── 5062.jpg\n",
      "│       │   ├── 5064.jpg\n",
      "│       │   ├── 5066.jpg\n",
      "│       │   ├── 5070.jpg\n",
      "│       │   ├── 5071.jpg\n",
      "│       │   ├── 5074.jpg\n",
      "│       │   ├── 5076.jpg\n",
      "│       │   ├── 5080.jpg\n",
      "│       │   ├── 5082.jpg\n",
      "│       │   ├── 5086.jpg\n",
      "│       │   ├── 5089.jpg\n",
      "│       │   ├── 508.jpg\n",
      "│       │   ├── 5093.jpg\n",
      "│       │   ├── 5095.jpg\n",
      "│       │   ├── 5096.jpg\n",
      "│       │   ├── 509.jpg\n",
      "│       │   ├── 5100.jpg\n",
      "│       │   ├── 5101.jpg\n",
      "│       │   ├── 5102.jpg\n",
      "│       │   ├── 5103.jpg\n",
      "│       │   ├── 5104.jpg\n",
      "│       │   ├── 5105.jpg\n",
      "│       │   ├── 5106.jpg\n",
      "│       │   ├── 5107.jpg\n",
      "│       │   ├── 5110.jpg\n",
      "│       │   ├── 5111.jpg\n",
      "│       │   ├── 5112.jpg\n",
      "│       │   ├── 5118.jpg\n",
      "│       │   ├── 5120.jpg\n",
      "│       │   ├── 5122.jpg\n",
      "│       │   ├── 5126.jpg\n",
      "│       │   ├── 5127.jpg\n",
      "│       │   ├── 512.jpg\n",
      "│       │   ├── 5130.jpg\n",
      "│       │   ├── 5132.jpg\n",
      "│       │   ├── 5133.jpg\n",
      "│       │   ├── 5134.jpg\n",
      "│       │   ├── 5137.jpg\n",
      "│       │   ├── 5143.jpg\n",
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      "│       │   ├── 5160.jpg\n",
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      "│       │   ├── 51.jpg\n",
      "│       │   ├── 5205.jpg\n",
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      "│       │   ├── 5220.jpg\n",
      "│       │   ├── 5221.jpg\n",
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      "│       │   ├── 5231.jpg\n",
      "│       │   ├── 5232.jpg\n",
      "│       │   ├── 5233.jpg\n",
      "│       │   ├── 5234.jpg\n",
      "│       │   ├── 5237.jpg\n",
      "│       │   ├── 5238.jpg\n",
      "│       │   ├── 5243.jpg\n",
      "│       │   ├── 5244.jpg\n",
      "│       │   ├── 5247.jpg\n",
      "│       │   ├── 5249.jpg\n",
      "│       │   ├── 524.jpg\n",
      "│       │   ├── 5250.jpg\n",
      "│       │   ├── 5256.jpg\n",
      "│       │   ├── 5257.jpg\n",
      "│       │   ├── 5260.jpg\n",
      "│       │   ├── 5262.jpg\n",
      "│       │   ├── 5263.jpg\n",
      "│       │   ├── 5265.jpg\n",
      "│       │   ├── 5266.jpg\n",
      "│       │   ├── 5267.jpg\n",
      "│       │   ├── 5269.jpg\n",
      "│       │   ├── 5272.jpg\n",
      "│       │   ├── 5274.jpg\n",
      "│       │   ├── 5276.jpg\n",
      "│       │   ├── 5278.jpg\n",
      "│       │   ├── 5281.jpg\n",
      "│       │   ├── 5282.jpg\n",
      "│       │   ├── 5283.jpg\n",
      "│       │   ├── 5284.jpg\n",
      "│       │   ├── 5285.jpg\n",
      "│       │   ├── 5286.jpg\n",
      "│       │   ├── 5287.jpg\n",
      "│       │   ├── 5288.jpg\n",
      "│       │   ├── 528.jpg\n",
      "│       │   ├── 5290.jpg\n",
      "│       │   ├── 5291.jpg\n",
      "│       │   ├── 5293.jpg\n",
      "│       │   ├── 5294.jpg\n",
      "│       │   ├── 5297.jpg\n",
      "│       │   ├── 5299.jpg\n",
      "│       │   ├── 52.jpg\n",
      "│       │   ├── 5301.jpg\n",
      "│       │   ├── 5302.jpg\n",
      "│       │   ├── 5303.jpg\n",
      "│       │   ├── 5304.jpg\n",
      "│       │   ├── 5308.jpg\n",
      "│       │   ├── 5309.jpg\n",
      "│       │   ├── 530.jpg\n",
      "│       │   ├── 5314.jpg\n",
      "│       │   ├── 5318.jpg\n",
      "│       │   ├── 5320.jpg\n",
      "│       │   ├── 5321.jpg\n",
      "│       │   ├── 5322.jpg\n",
      "│       │   ├── 5329.jpg\n",
      "│       │   ├── 532.jpg\n",
      "│       │   ├── 5331.jpg\n",
      "│       │   ├── 5334.jpg\n",
      "│       │   ├── 5338.jpg\n",
      "│       │   ├── 5340.jpg\n",
      "│       │   ├── 5341.jpg\n",
      "│       │   ├── 5344.jpg\n",
      "│       │   ├── 5348.jpg\n",
      "│       │   ├── 5349.jpg\n",
      "│       │   ├── 534.jpg\n",
      "│       │   ├── 5350.jpg\n",
      "│       │   ├── 5352.jpg\n",
      "│       │   ├── 5353.jpg\n",
      "│       │   ├── 5354.jpg\n",
      "│       │   ├── 5355.jpg\n",
      "│       │   ├── 5356.jpg\n",
      "│       │   ├── 5358.jpg\n",
      "│       │   ├── 5359.jpg\n",
      "│       │   ├── 535.jpg\n",
      "│       │   ├── 5362.jpg\n",
      "│       │   ├── 5364.jpg\n",
      "│       │   ├── 5367.jpg\n",
      "│       │   ├── 536.jpg\n",
      "│       │   ├── 5370.jpg\n",
      "│       │   ├── 5372.jpg\n",
      "│       │   ├── 5373.jpg\n",
      "│       │   ├── 5375.jpg\n",
      "│       │   ├── 5379.jpg\n",
      "│       │   ├── 5381.jpg\n",
      "│       │   ├── 5384.jpg\n",
      "│       │   ├── 5385.jpg\n",
      "│       │   ├── 5390.jpg\n",
      "│       │   ├── 5391.jpg\n",
      "│       │   ├── 5392.jpg\n",
      "│       │   ├── 5394.jpg\n",
      "│       │   ├── 5395.jpg\n",
      "│       │   ├── 5397.jpg\n",
      "│       │   ├── 5399.jpg\n",
      "│       │   ├── 539.jpg\n",
      "│       │   ├── 5400.jpg\n",
      "│       │   ├── 5407.jpg\n",
      "│       │   ├── 540.jpg\n",
      "│       │   ├── 5411.jpg\n",
      "│       │   ├── 5412.jpg\n",
      "│       │   ├── 5413.jpg\n",
      "│       │   ├── 5416.jpg\n",
      "│       │   ├── 5418.jpg\n",
      "│       │   ├── 5420.jpg\n",
      "│       │   ├── 5423.jpg\n",
      "│       │   ├── 5426.jpg\n",
      "│       │   ├── 5428.jpg\n",
      "│       │   ├── 542.jpg\n",
      "│       │   ├── 5433.jpg\n",
      "│       │   ├── 5435.jpg\n",
      "│       │   ├── 5437.jpg\n",
      "│       │   ├── 5438.jpg\n",
      "│       │   ├── 5441.jpg\n",
      "│       │   ├── 5448.jpg\n",
      "│       │   ├── 544.jpg\n",
      "│       │   ├── 5450.jpg\n",
      "│       │   ├── 5451.jpg\n",
      "│       │   ├── 5458.jpg\n",
      "│       │   ├── 5459.jpg\n",
      "│       │   ├── 5465.jpg\n",
      "│       │   ├── 5468.jpg\n",
      "│       │   ├── 5471.jpg\n",
      "│       │   ├── 5472.jpg\n",
      "│       │   ├── 5476.jpg\n",
      "│       │   ├── 5477.jpg\n",
      "│       │   ├── 5479.jpg\n",
      "│       │   ├── 5481.jpg\n",
      "│       │   ├── 5483.jpg\n",
      "│       │   ├── 5484.jpg\n",
      "│       │   ├── 5485.jpg\n",
      "│       │   ├── 5489.jpg\n",
      "│       │   ├── 548.jpg\n",
      "│       │   ├── 5492.jpg\n",
      "│       │   ├── 5493.jpg\n",
      "│       │   ├── 5495.jpg\n",
      "│       │   ├── 5496.jpg\n",
      "│       │   ├── 5497.jpg\n",
      "│       │   ├── 5499.jpg\n",
      "│       │   ├── 54.jpg\n",
      "│       │   ├── 5502.jpg\n",
      "│       │   ├── 5504.jpg\n",
      "│       │   ├── 5508.jpg\n",
      "│       │   ├── 5509.jpg\n",
      "│       │   ├── 550.jpg\n",
      "│       │   ├── 5510.jpg\n",
      "│       │   ├── 5511.jpg\n",
      "│       │   ├── 5513.jpg\n",
      "│       │   ├── 5515.jpg\n",
      "│       │   ├── 5516.jpg\n",
      "│       │   ├── 5518.jpg\n",
      "│       │   ├── 5520.jpg\n",
      "│       │   ├── 5523.jpg\n",
      "│       │   ├── 5529.jpg\n",
      "│       │   ├── 5531.jpg\n",
      "│       │   ├── 5532.jpg\n",
      "│       │   ├── 5534.jpg\n",
      "│       │   ├── 5535.jpg\n",
      "│       │   ├── 5536.jpg\n",
      "│       │   ├── 5538.jpg\n",
      "│       │   ├── 5546.jpg\n",
      "│       │   ├── 5549.jpg\n",
      "│       │   ├── 5550.jpg\n",
      "│       │   ├── 5555.jpg\n",
      "│       │   ├── 5557.jpg\n",
      "│       │   ├── 5558.jpg\n",
      "│       │   ├── 5559.jpg\n",
      "│       │   ├── 5561.jpg\n",
      "│       │   ├── 5566.jpg\n",
      "│       │   ├── 5567.jpg\n",
      "│       │   ├── 5568.jpg\n",
      "│       │   ├── 5569.jpg\n",
      "│       │   ├── 556.jpg\n",
      "│       │   ├── 5573.jpg\n",
      "│       │   ├── 5575.jpg\n",
      "│       │   ├── 5577.jpg\n",
      "│       │   ├── 557.jpg\n",
      "│       │   ├── 5580.jpg\n",
      "│       │   ├── 5582.jpg\n",
      "│       │   ├── 5583.jpg\n",
      "│       │   ├── 5586.jpg\n",
      "│       │   ├── 5588.jpg\n",
      "│       │   ├── 5589.jpg\n",
      "│       │   ├── 5591.jpg\n",
      "│       │   ├── 5592.jpg\n",
      "│       │   ├── 5593.jpg\n",
      "│       │   ├── 5594.jpg\n",
      "│       │   ├── 5596.jpg\n",
      "│       │   ├── 5597.jpg\n",
      "│       │   ├── 5598.jpg\n",
      "│       │   ├── 5599.jpg\n",
      "│       │   ├── 5600.jpg\n",
      "│       │   ├── 5608.jpg\n",
      "│       │   ├── 5609.jpg\n",
      "│       │   ├── 560.jpg\n",
      "│       │   ├── 5610.jpg\n",
      "│       │   ├── 5611.jpg\n",
      "│       │   ├── 5613.jpg\n",
      "│       │   ├── 5615.jpg\n",
      "│       │   ├── 5616.jpg\n",
      "│       │   ├── 5617.jpg\n",
      "│       │   ├── 5618.jpg\n",
      "│       │   ├── 5620.jpg\n",
      "│       │   ├── 5621.jpg\n",
      "│       │   ├── 5624.jpg\n",
      "│       │   ├── 5625.jpg\n",
      "│       │   ├── 5627.jpg\n",
      "│       │   ├── 5628.jpg\n",
      "│       │   ├── 5629.jpg\n",
      "│       │   ├── 5634.jpg\n",
      "│       │   ├── 5637.jpg\n",
      "│       │   ├── 5640.jpg\n",
      "│       │   ├── 5642.jpg\n",
      "│       │   ├── 5646.jpg\n",
      "│       │   ├── 5647.jpg\n",
      "│       │   ├── 5649.jpg\n",
      "│       │   ├── 564.jpg\n",
      "│       │   ├── 5653.jpg\n",
      "│       │   ├── 5654.jpg\n",
      "│       │   ├── 5656.jpg\n",
      "│       │   ├── 5658.jpg\n",
      "│       │   ├── 5659.jpg\n",
      "│       │   ├── 5664.jpg\n",
      "│       │   ├── 5665.jpg\n",
      "│       │   ├── 5666.jpg\n",
      "│       │   ├── 5667.jpg\n",
      "│       │   ├── 5669.jpg\n",
      "│       │   ├── 5671.jpg\n",
      "│       │   ├── 5674.jpg\n",
      "│       │   ├── 5676.jpg\n",
      "│       │   ├── 5677.jpg\n",
      "│       │   ├── 5680.jpg\n",
      "│       │   ├── 5681.jpg\n",
      "│       │   ├── 5682.jpg\n",
      "│       │   ├── 5683.jpg\n",
      "│       │   ├── 5685.jpg\n",
      "│       │   ├── 5689.jpg\n",
      "│       │   ├── 568.jpg\n",
      "│       │   ├── 5690.jpg\n",
      "│       │   ├── 5691.jpg\n",
      "│       │   ├── 5692.jpg\n",
      "│       │   ├── 5693.jpg\n",
      "│       │   ├── 5696.jpg\n",
      "│       │   ├── 5697.jpg\n",
      "│       │   ├── 5701.jpg\n",
      "│       │   ├── 5708.jpg\n",
      "│       │   ├── 5710.jpg\n",
      "│       │   ├── 5711.jpg\n",
      "│       │   ├── 5716.jpg\n",
      "│       │   ├── 5717.jpg\n",
      "│       │   ├── 5718.jpg\n",
      "│       │   ├── 571.jpg\n",
      "│       │   ├── 5723.jpg\n",
      "│       │   ├── 5724.jpg\n",
      "│       │   ├── 5725.jpg\n",
      "│       │   ├── 5727.jpg\n",
      "│       │   ├── 5728.jpg\n",
      "│       │   ├── 572.jpg\n",
      "│       │   ├── 5731.jpg\n",
      "│       │   ├── 5732.jpg\n",
      "│       │   ├── 5734.jpg\n",
      "│       │   ├── 5736.jpg\n",
      "│       │   ├── 5739.jpg\n",
      "│       │   ├── 5740.jpg\n",
      "│       │   ├── 5741.jpg\n",
      "│       │   ├── 5742.jpg\n",
      "│       │   ├── 5748.jpg\n",
      "│       │   ├── 5749.jpg\n",
      "│       │   ├── 574.jpg\n",
      "│       │   ├── 5750.jpg\n",
      "│       │   ├── 5752.jpg\n",
      "│       │   ├── 5754.jpg\n",
      "│       │   ├── 5756.jpg\n",
      "│       │   ├── 5757.jpg\n",
      "│       │   ├── 5758.jpg\n",
      "│       │   ├── 5759.jpg\n",
      "│       │   ├── 575.jpg\n",
      "│       │   ├── 5760.jpg\n",
      "│       │   ├── 5762.jpg\n",
      "│       │   ├── 5765.jpg\n",
      "│       │   ├── 5766.jpg\n",
      "│       │   ├── 5769.jpg\n",
      "│       │   ├── 576.jpg\n",
      "│       │   ├── 5770.jpg\n",
      "│       │   ├── 5775.jpg\n",
      "│       │   ├── 5776.jpg\n",
      "│       │   ├── 5777.jpg\n",
      "│       │   ├── 5780.jpg\n",
      "│       │   ├── 5782.jpg\n",
      "│       │   ├── 5783.jpg\n",
      "│       │   ├── 5784.jpg\n",
      "│       │   ├── 5785.jpg\n",
      "│       │   ├── 5786.jpg\n",
      "│       │   ├── 5787.jpg\n",
      "│       │   ├── 5789.jpg\n",
      "│       │   ├── 578.jpg\n",
      "│       │   ├── 5791.jpg\n",
      "│       │   ├── 5792.jpg\n",
      "│       │   ├── 5795.jpg\n",
      "│       │   ├── 5797.jpg\n",
      "│       │   ├── 5798.jpg\n",
      "│       │   ├── 5801.jpg\n",
      "│       │   ├── 5802.jpg\n",
      "│       │   ├── 5804.jpg\n",
      "│       │   ├── 5805.jpg\n",
      "│       │   ├── 5810.jpg\n",
      "│       │   ├── 5812.jpg\n",
      "│       │   ├── 5813.jpg\n",
      "│       │   ├── 5817.jpg\n",
      "│       │   ├── 5818.jpg\n",
      "│       │   ├── 581.jpg\n",
      "│       │   ├── 5820.jpg\n",
      "│       │   ├── 5821.jpg\n",
      "│       │   ├── 5822.jpg\n",
      "│       │   ├── 5825.jpg\n",
      "│       │   ├── 5827.jpg\n",
      "│       │   ├── 5829.jpg\n",
      "│       │   ├── 5833.jpg\n",
      "│       │   ├── 5834.jpg\n",
      "│       │   ├── 5839.jpg\n",
      "│       │   ├── 583.jpg\n",
      "│       │   ├── 5842.jpg\n",
      "│       │   ├── 5846.jpg\n",
      "│       │   ├── 5847.jpg\n",
      "│       │   ├── 5848.jpg\n",
      "│       │   ├── 5850.jpg\n",
      "│       │   ├── 5851.jpg\n",
      "│       │   ├── 5853.jpg\n",
      "│       │   ├── 5854.jpg\n",
      "│       │   ├── 5857.jpg\n",
      "│       │   ├── 5859.jpg\n",
      "│       │   ├── 585.jpg\n",
      "│       │   ├── 5865.jpg\n",
      "│       │   ├── 5866.jpg\n",
      "│       │   ├── 5868.jpg\n",
      "│       │   ├── 5869.jpg\n",
      "│       │   ├── 5871.jpg\n",
      "│       │   ├── 5872.jpg\n",
      "│       │   ├── 5874.jpg\n",
      "│       │   ├── 5876.jpg\n",
      "│       │   ├── 5877.jpg\n",
      "│       │   ├── 5879.jpg\n",
      "│       │   ├── 5883.jpg\n",
      "│       │   ├── 5886.jpg\n",
      "│       │   ├── 5889.jpg\n",
      "│       │   ├── 5893.jpg\n",
      "│       │   ├── 5895.jpg\n",
      "│       │   ├── 5896.jpg\n",
      "│       │   ├── 5897.jpg\n",
      "│       │   ├── 5899.jpg\n",
      "│       │   ├── 589.jpg\n",
      "│       │   ├── 5900.jpg\n",
      "│       │   ├── 5902.jpg\n",
      "│       │   ├── 5904.jpg\n",
      "│       │   ├── 5909.jpg\n",
      "│       │   ├── 590.jpg\n",
      "│       │   ├── 5913.jpg\n",
      "│       │   ├── 5915.jpg\n",
      "│       │   ├── 5918.jpg\n",
      "│       │   ├── 5921.jpg\n",
      "│       │   ├── 5923.jpg\n",
      "│       │   ├── 5924.jpg\n",
      "│       │   ├── 5925.jpg\n",
      "│       │   ├── 5926.jpg\n",
      "│       │   ├── 5927.jpg\n",
      "│       │   ├── 5928.jpg\n",
      "│       │   ├── 5930.jpg\n",
      "│       │   ├── 5933.jpg\n",
      "│       │   ├── 5935.jpg\n",
      "│       │   ├── 5938.jpg\n",
      "│       │   ├── 5941.jpg\n",
      "│       │   ├── 5942.jpg\n",
      "│       │   ├── 5946.jpg\n",
      "│       │   ├── 5949.jpg\n",
      "│       │   ├── 5950.jpg\n",
      "│       │   ├── 5951.jpg\n",
      "│       │   ├── 5953.jpg\n",
      "│       │   ├── 5959.jpg\n",
      "│       │   ├── 5961.jpg\n",
      "│       │   ├── 5962.jpg\n",
      "│       │   ├── 5966.jpg\n",
      "│       │   ├── 5967.jpg\n",
      "│       │   ├── 5969.jpg\n",
      "│       │   ├── 596.jpg\n",
      "│       │   ├── 5970.jpg\n",
      "│       │   ├── 5973.jpg\n",
      "│       │   ├── 5975.jpg\n",
      "│       │   ├── 5976.jpg\n",
      "│       │   ├── 5977.jpg\n",
      "│       │   ├── 597.jpg\n",
      "│       │   ├── 5981.jpg\n",
      "│       │   ├── 5982.jpg\n",
      "│       │   ├── 5985.jpg\n",
      "│       │   ├── 5987.jpg\n",
      "│       │   ├── 5988.jpg\n",
      "│       │   ├── 598.jpg\n",
      "│       │   ├── 5990.jpg\n",
      "│       │   ├── 5991.jpg\n",
      "│       │   ├── 5993.jpg\n",
      "│       │   ├── 5995.jpg\n",
      "│       │   ├── 5996.jpg\n",
      "│       │   ├── 59.jpg\n",
      "│       │   ├── 5.jpg\n",
      "│       │   ├── 6000.jpg\n",
      "│       │   ├── 6004.jpg\n",
      "│       │   ├── 6005.jpg\n",
      "│       │   ├── 6007.jpg\n",
      "│       │   ├── 6009.jpg\n",
      "│       │   ├── 6010.jpg\n",
      "│       │   ├── 6014.jpg\n",
      "│       │   ├── 6015.jpg\n",
      "│       │   ├── 601.jpg\n",
      "│       │   ├── 6020.jpg\n",
      "│       │   ├── 6021.jpg\n",
      "│       │   ├── 6024.jpg\n",
      "│       │   ├── 6025.jpg\n",
      "│       │   ├── 6026.jpg\n",
      "│       │   ├── 6029.jpg\n",
      "│       │   ├── 602.jpg\n",
      "│       │   ├── 6030.jpg\n",
      "│       │   ├── 6032.jpg\n",
      "│       │   ├── 6035.jpg\n",
      "│       │   ├── 6038.jpg\n",
      "│       │   ├── 6041.jpg\n",
      "│       │   ├── 6043.jpg\n",
      "│       │   ├── 6045.jpg\n",
      "│       │   ├── 6047.jpg\n",
      "│       │   ├── 604.jpg\n",
      "│       │   ├── 6053.jpg\n",
      "│       │   ├── 6056.jpg\n",
      "│       │   ├── 6059.jpg\n",
      "│       │   ├── 6060.jpg\n",
      "│       │   ├── 6062.jpg\n",
      "│       │   ├── 6064.jpg\n",
      "│       │   ├── 6070.jpg\n",
      "│       │   ├── 6071.jpg\n",
      "│       │   ├── 6073.jpg\n",
      "│       │   ├── 6076.jpg\n",
      "│       │   ├── 6077.jpg\n",
      "│       │   ├── 6078.jpg\n",
      "│       │   ├── 607.jpg\n",
      "│       │   ├── 6082.jpg\n",
      "│       │   ├── 6084.jpg\n",
      "│       │   ├── 6085.jpg\n",
      "│       │   ├── 6086.jpg\n",
      "│       │   ├── 6090.jpg\n",
      "│       │   ├── 6091.jpg\n",
      "│       │   ├── 6093.jpg\n",
      "│       │   ├── 6095.jpg\n",
      "│       │   ├── 6098.jpg\n",
      "│       │   ├── 6099.jpg\n",
      "│       │   ├── 6102.jpg\n",
      "│       │   ├── 6103.jpg\n",
      "│       │   ├── 6107.jpg\n",
      "│       │   ├── 6109.jpg\n",
      "│       │   ├── 610.jpg\n",
      "│       │   ├── 6110.jpg\n",
      "│       │   ├── 6112.jpg\n",
      "│       │   ├── 6113.jpg\n",
      "│       │   ├── 6118.jpg\n",
      "│       │   ├── 611.jpg\n",
      "│       │   ├── 6120.jpg\n",
      "│       │   ├── 6121.jpg\n",
      "│       │   ├── 6122.jpg\n",
      "│       │   ├── 6124.jpg\n",
      "│       │   ├── 6126.jpg\n",
      "│       │   ├── 6127.jpg\n",
      "│       │   ├── 6130.jpg\n",
      "│       │   ├── 6135.jpg\n",
      "│       │   ├── 6136.jpg\n",
      "│       │   ├── 6137.jpg\n",
      "│       │   ├── 6139.jpg\n",
      "│       │   ├── 6141.jpg\n",
      "│       │   ├── 6142.jpg\n",
      "│       │   ├── 6144.jpg\n",
      "│       │   ├── 6146.jpg\n",
      "│       │   ├── 6148.jpg\n",
      "│       │   ├── 6150.jpg\n",
      "│       │   ├── 6153.jpg\n",
      "│       │   ├── 6154.jpg\n",
      "│       │   ├── 6158.jpg\n",
      "│       │   ├── 6160.jpg\n",
      "│       │   ├── 6164.jpg\n",
      "│       │   ├── 6165.jpg\n",
      "│       │   ├── 6169.jpg\n",
      "│       │   ├── 616.jpg\n",
      "│       │   ├── 6175.jpg\n",
      "│       │   ├── 6176.jpg\n",
      "│       │   ├── 6177.jpg\n",
      "│       │   ├── 6180.jpg\n",
      "│       │   ├── 6183.jpg\n",
      "│       │   ├── 6184.jpg\n",
      "│       │   ├── 6186.jpg\n",
      "│       │   ├── 6188.jpg\n",
      "│       │   ├── 6189.jpg\n",
      "│       │   ├── 6190.jpg\n",
      "│       │   ├── 6193.jpg\n",
      "│       │   ├── 6196.jpg\n",
      "│       │   ├── 6197.jpg\n",
      "│       │   ├── 6198.jpg\n",
      "│       │   ├── 61.jpg\n",
      "│       │   ├── 6201.jpg\n",
      "│       │   ├── 6202.jpg\n",
      "│       │   ├── 6203.jpg\n",
      "│       │   ├── 6208.jpg\n",
      "│       │   ├── 6213.jpg\n",
      "│       │   ├── 6218.jpg\n",
      "│       │   ├── 6219.jpg\n",
      "│       │   ├── 621.jpg\n",
      "│       │   ├── 6220.jpg\n",
      "│       │   ├── 6222.jpg\n",
      "│       │   ├── 6223.jpg\n",
      "│       │   ├── 6224.jpg\n",
      "│       │   ├── 6225.jpg\n",
      "│       │   ├── 6226.jpg\n",
      "│       │   ├── 6229.jpg\n",
      "│       │   ├── 622.jpg\n",
      "│       │   ├── 6232.jpg\n",
      "│       │   ├── 6233.jpg\n",
      "│       │   ├── 6234.jpg\n",
      "│       │   ├── 6235.jpg\n",
      "│       │   ├── 6236.jpg\n",
      "│       │   ├── 6237.jpg\n",
      "│       │   ├── 6239.jpg\n",
      "│       │   ├── 6243.jpg\n",
      "│       │   ├── 6245.jpg\n",
      "│       │   ├── 6247.jpg\n",
      "│       │   ├── 6250.jpg\n",
      "│       │   ├── 6251.jpg\n",
      "│       │   ├── 6253.jpg\n",
      "│       │   ├── 6256.jpg\n",
      "│       │   ├── 6257.jpg\n",
      "│       │   ├── 6258.jpg\n",
      "│       │   ├── 625.jpg\n",
      "│       │   ├── 6260.jpg\n",
      "│       │   ├── 6264.jpg\n",
      "│       │   ├── 6265.jpg\n",
      "│       │   ├── 626.jpg\n",
      "│       │   ├── 6270.jpg\n",
      "│       │   ├── 6273.jpg\n",
      "│       │   ├── 6275.jpg\n",
      "│       │   ├── 6276.jpg\n",
      "│       │   ├── 6279.jpg\n",
      "│       │   ├── 6280.jpg\n",
      "│       │   ├── 6285.jpg\n",
      "│       │   ├── 6286.jpg\n",
      "│       │   ├── 6288.jpg\n",
      "│       │   ├── 628.jpg\n",
      "│       │   ├── 6290.jpg\n",
      "│       │   ├── 6293.jpg\n",
      "│       │   ├── 6296.jpg\n",
      "│       │   ├── 6301.jpg\n",
      "│       │   ├── 6303.jpg\n",
      "│       │   ├── 6304.jpg\n",
      "│       │   ├── 6305.jpg\n",
      "│       │   ├── 6306.jpg\n",
      "│       │   ├── 6307.jpg\n",
      "│       │   ├── 6308.jpg\n",
      "│       │   ├── 630.jpg\n",
      "│       │   ├── 6321.jpg\n",
      "│       │   ├── 6322.jpg\n",
      "│       │   ├── 6323.jpg\n",
      "│       │   ├── 6324.jpg\n",
      "│       │   ├── 6325.jpg\n",
      "│       │   ├── 6327.jpg\n",
      "│       │   ├── 6328.jpg\n",
      "│       │   ├── 6331.jpg\n",
      "│       │   ├── 6335.jpg\n",
      "│       │   ├── 6336.jpg\n",
      "│       │   ├── 6337.jpg\n",
      "│       │   ├── 6339.jpg\n",
      "│       │   ├── 6340.jpg\n",
      "│       │   ├── 6341.jpg\n",
      "│       │   ├── 6345.jpg\n",
      "│       │   ├── 6347.jpg\n",
      "│       │   ├── 6348.jpg\n",
      "│       │   ├── 634.jpg\n",
      "│       │   ├── 6352.jpg\n",
      "│       │   ├── 6353.jpg\n",
      "│       │   ├── 635.jpg\n",
      "│       │   ├── 6360.jpg\n",
      "│       │   ├── 6363.jpg\n",
      "│       │   ├── 6364.jpg\n",
      "│       │   ├── 6366.jpg\n",
      "│       │   ├── 6367.jpg\n",
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      "│       │   ├── 6372.jpg\n",
      "│       │   ├── 6374.jpg\n",
      "│       │   ├── 6376.jpg\n",
      "│       │   ├── 6377.jpg\n",
      "│       │   ├── 6378.jpg\n",
      "│       │   ├── 6380.jpg\n",
      "│       │   ├── 6381.jpg\n",
      "│       │   ├── 6382.jpg\n",
      "│       │   ├── 6387.jpg\n",
      "│       │   ├── 6388.jpg\n",
      "│       │   ├── 638.jpg\n",
      "│       │   ├── 6393.jpg\n",
      "│       │   ├── 6394.jpg\n",
      "│       │   ├── 6395.jpg\n",
      "│       │   ├── 6397.jpg\n",
      "│       │   ├── 639.jpg\n",
      "│       │   ├── 63.jpg\n",
      "│       │   ├── 6403.jpg\n",
      "│       │   ├── 6404.jpg\n",
      "│       │   ├── 6409.jpg\n",
      "│       │   ├── 6411.jpg\n",
      "│       │   ├── 6412.jpg\n",
      "│       │   ├── 6415.jpg\n",
      "│       │   ├── 6416.jpg\n",
      "│       │   ├── 6421.jpg\n",
      "│       │   ├── 6422.jpg\n",
      "│       │   ├── 6424.jpg\n",
      "│       │   ├── 6427.jpg\n",
      "│       │   ├── 6428.jpg\n",
      "│       │   ├── 6429.jpg\n",
      "│       │   ├── 6430.jpg\n",
      "│       │   ├── 6432.jpg\n",
      "│       │   ├── 6438.jpg\n",
      "│       │   ├── 643.jpg\n",
      "│       │   ├── 6445.jpg\n",
      "│       │   ├── 6447.jpg\n",
      "│       │   ├── 644.jpg\n",
      "│       │   ├── 6451.jpg\n",
      "│       │   ├── 6454.jpg\n",
      "│       │   ├── 6460.jpg\n",
      "│       │   ├── 6464.jpg\n",
      "│       │   ├── 6467.jpg\n",
      "│       │   ├── 6468.jpg\n",
      "│       │   ├── 6471.jpg\n",
      "│       │   ├── 6472.jpg\n",
      "│       │   ├── 6475.jpg\n",
      "│       │   ├── 6476.jpg\n",
      "│       │   ├── 6478.jpg\n",
      "│       │   ├── 647.jpg\n",
      "│       │   ├── 6484.jpg\n",
      "│       │   ├── 6485.jpg\n",
      "│       │   ├── 6488.jpg\n",
      "│       │   ├── 648.jpg\n",
      "│       │   ├── 6494.jpg\n",
      "│       │   ├── 6497.jpg\n",
      "│       │   ├── 6500.jpg\n",
      "│       │   ├── 6502.jpg\n",
      "│       │   ├── 6505.jpg\n",
      "│       │   ├── 6509.jpg\n",
      "│       │   ├── 6512.jpg\n",
      "│       │   ├── 6514.jpg\n",
      "│       │   ├── 6515.jpg\n",
      "│       │   ├── 6516.jpg\n",
      "│       │   ├── 6517.jpg\n",
      "│       │   ├── 6518.jpg\n",
      "│       │   ├── 6519.jpg\n",
      "│       │   ├── 651.jpg\n",
      "│       │   ├── 6520.jpg\n",
      "│       │   ├── 6521.jpg\n",
      "│       │   ├── 6523.jpg\n",
      "│       │   ├── 6524.jpg\n",
      "│       │   ├── 652.jpg\n",
      "│       │   ├── 6531.jpg\n",
      "│       │   ├── 6532.jpg\n",
      "│       │   ├── 6540.jpg\n",
      "│       │   ├── 6541.jpg\n",
      "│       │   ├── 6543.jpg\n",
      "│       │   ├── 6545.jpg\n",
      "│       │   ├── 6546.jpg\n",
      "│       │   ├── 6547.jpg\n",
      "│       │   ├── 654.jpg\n",
      "│       │   ├── 6552.jpg\n",
      "│       │   ├── 6553.jpg\n",
      "│       │   ├── 6554.jpg\n",
      "│       │   ├── 6557.jpg\n",
      "│       │   ├── 6558.jpg\n",
      "│       │   ├── 6559.jpg\n",
      "│       │   ├── 6561.jpg\n",
      "│       │   ├── 6563.jpg\n",
      "│       │   ├── 6564.jpg\n",
      "│       │   ├── 6567.jpg\n",
      "│       │   ├── 6569.jpg\n",
      "│       │   ├── 6571.jpg\n",
      "│       │   ├── 6572.jpg\n",
      "│       │   ├── 6573.jpg\n",
      "│       │   ├── 6576.jpg\n",
      "│       │   ├── 6578.jpg\n",
      "│       │   ├── 6579.jpg\n",
      "│       │   ├── 6580.jpg\n",
      "│       │   ├── 6582.jpg\n",
      "│       │   ├── 6584.jpg\n",
      "│       │   ├── 6585.jpg\n",
      "│       │   ├── 6586.jpg\n",
      "│       │   ├── 6587.jpg\n",
      "│       │   ├── 6588.jpg\n",
      "│       │   ├── 6589.jpg\n",
      "│       │   ├── 658.jpg\n",
      "│       │   ├── 6592.jpg\n",
      "│       │   ├── 6595.jpg\n",
      "│       │   ├── 6596.jpg\n",
      "│       │   ├── 65.jpg\n",
      "│       │   ├── 6601.jpg\n",
      "│       │   ├── 6605.jpg\n",
      "│       │   ├── 6606.jpg\n",
      "│       │   ├── 6607.jpg\n",
      "│       │   ├── 6609.jpg\n",
      "│       │   ├── 6610.jpg\n",
      "│       │   ├── 6612.jpg\n",
      "│       │   ├── 6613.jpg\n",
      "│       │   ├── 6614.jpg\n",
      "│       │   ├── 6616.jpg\n",
      "│       │   ├── 6617.jpg\n",
      "│       │   ├── 6618.jpg\n",
      "│       │   ├── 6619.jpg\n",
      "│       │   ├── 6620.jpg\n",
      "│       │   ├── 6621.jpg\n",
      "│       │   ├── 6622.jpg\n",
      "│       │   ├── 6628.jpg\n",
      "│       │   ├── 6629.jpg\n",
      "│       │   ├── 6630.jpg\n",
      "│       │   ├── 6634.jpg\n",
      "│       │   ├── 6635.jpg\n",
      "│       │   ├── 6639.jpg\n",
      "│       │   ├── 6643.jpg\n",
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      "│       │   ├── 6655.jpg\n",
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      "│       │   ├── 6669.jpg\n",
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      "│       │   ├── 6721.jpg\n",
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      "│       │   ├── 6775.jpg\n",
      "│       │   ├── 6777.jpg\n",
      "│       │   ├── 677.jpg\n",
      "│       │   ├── 6780.jpg\n",
      "│       │   ├── 6784.jpg\n",
      "│       │   ├── 6787.jpg\n",
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      "│       │   ├── 678.jpg\n",
      "│       │   ├── 6790.jpg\n",
      "│       │   ├── 6791.jpg\n",
      "│       │   ├── 6794.jpg\n",
      "│       │   ├── 6795.jpg\n",
      "│       │   ├── 6796.jpg\n",
      "│       │   ├── 6800.jpg\n",
      "│       │   ├── 6801.jpg\n",
      "│       │   ├── 6803.jpg\n",
      "│       │   ├── 6805.jpg\n",
      "│       │   ├── 6807.jpg\n",
      "│       │   ├── 6809.jpg\n",
      "│       │   ├── 680.jpg\n",
      "│       │   ├── 6817.jpg\n",
      "│       │   ├── 6818.jpg\n",
      "│       │   ├── 6819.jpg\n",
      "│       │   ├── 681.jpg\n",
      "│       │   ├── 6824.jpg\n",
      "│       │   ├── 6826.jpg\n",
      "│       │   ├── 682.jpg\n",
      "│       │   ├── 6833.jpg\n",
      "│       │   ├── 6834.jpg\n",
      "│       │   ├── 6836.jpg\n",
      "│       │   ├── 6838.jpg\n",
      "│       │   ├── 683.jpg\n",
      "│       │   ├── 6841.jpg\n",
      "│       │   ├── 6842.jpg\n",
      "│       │   ├── 6843.jpg\n",
      "│       │   ├── 6844.jpg\n",
      "│       │   ├── 6846.jpg\n",
      "│       │   ├── 6847.jpg\n",
      "│       │   ├── 6848.jpg\n",
      "│       │   ├── 6850.jpg\n",
      "│       │   ├── 6851.jpg\n",
      "│       │   ├── 6853.jpg\n",
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      "│       │   ├── 6856.jpg\n",
      "│       │   ├── 6857.jpg\n",
      "│       │   ├── 685.jpg\n",
      "│       │   ├── 6862.jpg\n",
      "│       │   ├── 6865.jpg\n",
      "│       │   ├── 6866.jpg\n",
      "│       │   ├── 6868.jpg\n",
      "│       │   ├── 6869.jpg\n",
      "│       │   ├── 6874.jpg\n",
      "│       │   ├── 6875.jpg\n",
      "│       │   ├── 6876.jpg\n",
      "│       │   ├── 6877.jpg\n",
      "│       │   ├── 6879.jpg\n",
      "│       │   ├── 6880.jpg\n",
      "│       │   ├── 6883.jpg\n",
      "│       │   ├── 6886.jpg\n",
      "│       │   ├── 6894.jpg\n",
      "│       │   ├── 6895.jpg\n",
      "│       │   ├── 6900.jpg\n",
      "│       │   ├── 6904.jpg\n",
      "│       │   ├── 6907.jpg\n",
      "│       │   ├── 6913.jpg\n",
      "│       │   ├── 6916.jpg\n",
      "│       │   ├── 6918.jpg\n",
      "│       │   ├── 6921.jpg\n",
      "│       │   ├── 6922.jpg\n",
      "│       │   ├── 6924.jpg\n",
      "│       │   ├── 6928.jpg\n",
      "│       │   ├── 6929.jpg\n",
      "│       │   ├── 692.jpg\n",
      "│       │   ├── 6932.jpg\n",
      "│       │   ├── 6936.jpg\n",
      "│       │   ├── 6938.jpg\n",
      "│       │   ├── 693.jpg\n",
      "│       │   ├── 6941.jpg\n",
      "│       │   ├── 6945.jpg\n",
      "│       │   ├── 6948.jpg\n",
      "│       │   ├── 6949.jpg\n",
      "│       │   ├── 694.jpg\n",
      "│       │   ├── 6952.jpg\n",
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      "│       │   ├── 695.jpg\n",
      "│       │   ├── 6960.jpg\n",
      "│       │   ├── 6962.jpg\n",
      "│       │   ├── 6966.jpg\n",
      "│       │   ├── 6967.jpg\n",
      "│       │   ├── 6968.jpg\n",
      "│       │   ├── 6969.jpg\n",
      "│       │   ├── 6971.jpg\n",
      "│       │   ├── 6972.jpg\n",
      "│       │   ├── 6975.jpg\n",
      "│       │   ├── 6977.jpg\n",
      "│       │   ├── 6978.jpg\n",
      "│       │   ├── 6980.jpg\n",
      "│       │   ├── 6981.jpg\n",
      "│       │   ├── 6983.jpg\n",
      "│       │   ├── 6984.jpg\n",
      "│       │   ├── 6985.jpg\n",
      "│       │   ├── 6986.jpg\n",
      "│       │   ├── 6987.jpg\n",
      "│       │   ├── 6988.jpg\n",
      "│       │   ├── 698.jpg\n",
      "│       │   ├── 6992.jpg\n",
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      "│       │   ├── 6995.jpg\n",
      "│       │   ├── 6996.jpg\n",
      "│       │   ├── 6997.jpg\n",
      "│       │   ├── 6998.jpg\n",
      "│       │   ├── 6999.jpg\n",
      "│       │   ├── 69.jpg\n",
      "│       │   ├── 7003.jpg\n",
      "│       │   ├── 7005.jpg\n",
      "│       │   ├── 7006.jpg\n",
      "│       │   ├── 7007.jpg\n",
      "│       │   ├── 7010.jpg\n",
      "│       │   ├── 7016.jpg\n",
      "│       │   ├── 7017.jpg\n",
      "│       │   ├── 7019.jpg\n",
      "│       │   ├── 701.jpg\n",
      "│       │   ├── 7021.jpg\n",
      "│       │   ├── 7026.jpg\n",
      "│       │   ├── 7028.jpg\n",
      "│       │   ├── 7029.jpg\n",
      "│       │   ├── 702.jpg\n",
      "│       │   ├── 7032.jpg\n",
      "│       │   ├── 7041.jpg\n",
      "│       │   ├── 7042.jpg\n",
      "│       │   ├── 7043.jpg\n",
      "│       │   ├── 7044.jpg\n",
      "│       │   ├── 7046.jpg\n",
      "│       │   ├── 7047.jpg\n",
      "│       │   ├── 7048.jpg\n",
      "│       │   ├── 7049.jpg\n",
      "│       │   ├── 704.jpg\n",
      "│       │   ├── 7050.jpg\n",
      "│       │   ├── 7053.jpg\n",
      "│       │   ├── 7055.jpg\n",
      "│       │   ├── 7056.jpg\n",
      "│       │   ├── 7059.jpg\n",
      "│       │   ├── 705.jpg\n",
      "│       │   ├── 7060.jpg\n",
      "│       │   ├── 7062.jpg\n",
      "│       │   ├── 7064.jpg\n",
      "│       │   ├── 7067.jpg\n",
      "│       │   ├── 7069.jpg\n",
      "│       │   ├── 706.jpg\n",
      "│       │   ├── 7070.jpg\n",
      "│       │   ├── 7071.jpg\n",
      "│       │   ├── 7072.jpg\n",
      "│       │   ├── 7077.jpg\n",
      "│       │   ├── 7078.jpg\n",
      "│       │   ├── 7080.jpg\n",
      "│       │   ├── 7081.jpg\n",
      "│       │   ├── 7082.jpg\n",
      "│       │   ├── 7088.jpg\n",
      "│       │   ├── 7097.jpg\n",
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      "│       │   ├── 7099.jpg\n",
      "│       │   ├── 709.jpg\n",
      "│       │   ├── 7107.jpg\n",
      "│       │   ├── 710.jpg\n",
      "│       │   ├── 7111.jpg\n",
      "│       │   ├── 7112.jpg\n",
      "│       │   ├── 7113.jpg\n",
      "│       │   ├── 7117.jpg\n",
      "│       │   ├── 7120.jpg\n",
      "│       │   ├── 7123.jpg\n",
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      "│       │   ├── 7128.jpg\n",
      "│       │   ├── 7131.jpg\n",
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      "│       │   ├── 7135.jpg\n",
      "│       │   ├── 7138.jpg\n",
      "│       │   ├── 713.jpg\n",
      "│       │   ├── 7141.jpg\n",
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      "│       │   ├── 7162.jpg\n",
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      "│       │   ├── 7169.jpg\n",
      "│       │   ├── 716.jpg\n",
      "│       │   ├── 7170.jpg\n",
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      "│       │   ├── 7176.jpg\n",
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      "│       │   ├── 7181.jpg\n",
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      "│       │   ├── 7184.jpg\n",
      "│       │   ├── 7188.jpg\n",
      "│       │   ├── 7190.jpg\n",
      "│       │   ├── 719.jpg\n",
      "│       │   ├── 7200.jpg\n",
      "│       │   ├── 7201.jpg\n",
      "│       │   ├── 7203.jpg\n",
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      "│       │   ├── 7209.jpg\n",
      "│       │   ├── 720.jpg\n",
      "│       │   ├── 7212.jpg\n",
      "│       │   ├── 7213.jpg\n",
      "│       │   ├── 7218.jpg\n",
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      "│       │   ├── 7221.jpg\n",
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      "│       │   ├── 7228.jpg\n",
      "│       │   ├── 7230.jpg\n",
      "│       │   ├── 7232.jpg\n",
      "│       │   ├── 7233.jpg\n",
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      "│       │   ├── 7240.jpg\n",
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      "│       │   ├── 7245.jpg\n",
      "│       │   ├── 7249.jpg\n",
      "│       │   ├── 7250.jpg\n",
      "│       │   ├── 7252.jpg\n",
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      "│       │   ├── 7256.jpg\n",
      "│       │   ├── 7260.jpg\n",
      "│       │   ├── 7262.jpg\n",
      "│       │   ├── 7264.jpg\n",
      "│       │   ├── 7266.jpg\n",
      "│       │   ├── 7269.jpg\n",
      "│       │   ├── 7270.jpg\n",
      "│       │   ├── 7271.jpg\n",
      "│       │   ├── 7273.jpg\n",
      "│       │   ├── 7279.jpg\n",
      "│       │   ├── 727.jpg\n",
      "│       │   ├── 7282.jpg\n",
      "│       │   ├── 7283.jpg\n",
      "│       │   ├── 7286.jpg\n",
      "│       │   ├── 728.jpg\n",
      "│       │   ├── 7293.jpg\n",
      "│       │   ├── 7294.jpg\n",
      "│       │   ├── 7295.jpg\n",
      "│       │   ├── 7297.jpg\n",
      "│       │   ├── 72.jpg\n",
      "│       │   ├── 7300.jpg\n",
      "│       │   ├── 7303.jpg\n",
      "│       │   ├── 7306.jpg\n",
      "│       │   ├── 7307.jpg\n",
      "│       │   ├── 7308.jpg\n",
      "│       │   ├── 7309.jpg\n",
      "│       │   ├── 730.jpg\n",
      "│       │   ├── 7313.jpg\n",
      "│       │   ├── 7314.jpg\n",
      "│       │   ├── 7317.jpg\n",
      "│       │   ├── 7318.jpg\n",
      "│       │   ├── 7320.jpg\n",
      "│       │   ├── 7322.jpg\n",
      "│       │   ├── 7324.jpg\n",
      "│       │   ├── 7325.jpg\n",
      "│       │   ├── 7326.jpg\n",
      "│       │   ├── 7328.jpg\n",
      "│       │   ├── 732.jpg\n",
      "│       │   ├── 7331.jpg\n",
      "│       │   ├── 7339.jpg\n",
      "│       │   ├── 7340.jpg\n",
      "│       │   ├── 7342.jpg\n",
      "│       │   ├── 734.jpg\n",
      "│       │   ├── 7350.jpg\n",
      "│       │   ├── 7352.jpg\n",
      "│       │   ├── 7353.jpg\n",
      "│       │   ├── 7354.jpg\n",
      "│       │   ├── 7355.jpg\n",
      "│       │   ├── 735.jpg\n",
      "│       │   ├── 7360.jpg\n",
      "│       │   ├── 7364.jpg\n",
      "│       │   ├── 7365.jpg\n",
      "│       │   ├── 7367.jpg\n",
      "│       │   ├── 7368.jpg\n",
      "│       │   ├── 7369.jpg\n",
      "│       │   ├── 7370.jpg\n",
      "│       │   ├── 7372.jpg\n",
      "│       │   ├── 7374.jpg\n",
      "│       │   ├── 7375.jpg\n",
      "│       │   ├── 7376.jpg\n",
      "│       │   ├── 7377.jpg\n",
      "│       │   ├── 7379.jpg\n",
      "│       │   ├── 7385.jpg\n",
      "│       │   ├── 7386.jpg\n",
      "│       │   ├── 7387.jpg\n",
      "│       │   ├── 7394.jpg\n",
      "│       │   ├── 7397.jpg\n",
      "│       │   ├── 7398.jpg\n",
      "│       │   ├── 7399.jpg\n",
      "│       │   ├── 739.jpg\n",
      "│       │   ├── 73.jpg\n",
      "│       │   ├── 7402.jpg\n",
      "│       │   ├── 7403.jpg\n",
      "│       │   ├── 7404.jpg\n",
      "│       │   ├── 7408.jpg\n",
      "│       │   ├── 7409.jpg\n",
      "│       │   ├── 740.jpg\n",
      "│       │   ├── 7410.jpg\n",
      "│       │   ├── 7411.jpg\n",
      "│       │   ├── 7412.jpg\n",
      "│       │   ├── 7417.jpg\n",
      "│       │   ├── 7418.jpg\n",
      "│       │   ├── 7420.jpg\n",
      "│       │   ├── 7421.jpg\n",
      "│       │   ├── 7422.jpg\n",
      "│       │   ├── 7426.jpg\n",
      "│       │   ├── 7429.jpg\n",
      "│       │   ├── 7432.jpg\n",
      "│       │   ├── 7437.jpg\n",
      "│       │   ├── 7438.jpg\n",
      "│       │   ├── 7441.jpg\n",
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      "│       │   ├── 7461.jpg\n",
      "│       │   ├── 7464.jpg\n",
      "│       │   ├── 7466.jpg\n",
      "│       │   ├── 7467.jpg\n",
      "│       │   ├── 7469.jpg\n",
      "│       │   ├── 746.jpg\n",
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      "│       │   ├── 7480.jpg\n",
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      "│       │   ├── 7518.jpg\n",
      "│       │   ├── 7521.jpg\n",
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      "│       │   ├── 7524.jpg\n",
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      "│       │   ├── 7531.jpg\n",
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      "│       │   ├── 7542.jpg\n",
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      "│       │   ├── 7544.jpg\n",
      "│       │   ├── 7547.jpg\n",
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      "│       │   ├── 754.jpg\n",
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      "│       │   ├── 7564.jpg\n",
      "│       │   ├── 7566.jpg\n",
      "│       │   ├── 7567.jpg\n",
      "│       │   ├── 7569.jpg\n",
      "│       │   ├── 7570.jpg\n",
      "│       │   ├── 7572.jpg\n",
      "│       │   ├── 7574.jpg\n",
      "│       │   ├── 7575.jpg\n",
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      "│       │   ├── 7578.jpg\n",
      "│       │   ├── 7579.jpg\n",
      "│       │   ├── 7580.jpg\n",
      "│       │   ├── 7584.jpg\n",
      "│       │   ├── 7585.jpg\n",
      "│       │   ├── 7587.jpg\n",
      "│       │   ├── 7594.jpg\n",
      "│       │   ├── 7597.jpg\n",
      "│       │   ├── 7598.jpg\n",
      "│       │   ├── 7599.jpg\n",
      "│       │   ├── 759.jpg\n",
      "│       │   ├── 75.jpg\n",
      "│       │   ├── 7602.jpg\n",
      "│       │   ├── 7604.jpg\n",
      "│       │   ├── 7605.jpg\n",
      "│       │   ├── 7606.jpg\n",
      "│       │   ├── 7609.jpg\n",
      "│       │   ├── 7614.jpg\n",
      "│       │   ├── 7617.jpg\n",
      "│       │   ├── 7619.jpg\n",
      "│       │   ├── 7620.jpg\n",
      "│       │   ├── 7623.jpg\n",
      "│       │   ├── 7629.jpg\n",
      "│       │   ├── 7631.jpg\n",
      "│       │   ├── 7632.jpg\n",
      "│       │   ├── 7634.jpg\n",
      "│       │   ├── 7636.jpg\n",
      "│       │   ├── 7637.jpg\n",
      "│       │   ├── 7641.jpg\n",
      "│       │   ├── 7643.jpg\n",
      "│       │   ├── 7645.jpg\n",
      "│       │   ├── 7647.jpg\n",
      "│       │   ├── 7649.jpg\n",
      "│       │   ├── 7653.jpg\n",
      "│       │   ├── 7656.jpg\n",
      "│       │   ├── 7659.jpg\n",
      "│       │   ├── 765.jpg\n",
      "│       │   ├── 7660.jpg\n",
      "│       │   ├── 7663.jpg\n",
      "│       │   ├── 7667.jpg\n",
      "│       │   ├── 7669.jpg\n",
      "│       │   ├── 7670.jpg\n",
      "│       │   ├── 7674.jpg\n",
      "│       │   ├── 7675.jpg\n",
      "│       │   ├── 7677.jpg\n",
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      "│       │   ├── 7679.jpg\n",
      "│       │   ├── 767.jpg\n",
      "│       │   ├── 7680.jpg\n",
      "│       │   ├── 7682.jpg\n",
      "│       │   ├── 7683.jpg\n",
      "│       │   ├── 7684.jpg\n",
      "│       │   ├── 7685.jpg\n",
      "│       │   ├── 7687.jpg\n",
      "│       │   ├── 7688.jpg\n",
      "│       │   ├── 7689.jpg\n",
      "│       │   ├── 7690.jpg\n",
      "│       │   ├── 7694.jpg\n",
      "│       │   ├── 7696.jpg\n",
      "│       │   ├── 7698.jpg\n",
      "│       │   ├── 769.jpg\n",
      "│       │   ├── 7702.jpg\n",
      "│       │   ├── 7704.jpg\n",
      "│       │   ├── 7705.jpg\n",
      "│       │   ├── 7707.jpg\n",
      "│       │   ├── 7709.jpg\n",
      "│       │   ├── 7710.jpg\n",
      "│       │   ├── 7712.jpg\n",
      "│       │   ├── 7716.jpg\n",
      "│       │   ├── 7717.jpg\n",
      "│       │   ├── 7719.jpg\n",
      "│       │   ├── 771.jpg\n",
      "│       │   ├── 7720.jpg\n",
      "│       │   ├── 7721.jpg\n",
      "│       │   ├── 7722.jpg\n",
      "│       │   ├── 7723.jpg\n",
      "│       │   ├── 7725.jpg\n",
      "│       │   ├── 7727.jpg\n",
      "│       │   ├── 7728.jpg\n",
      "│       │   ├── 7729.jpg\n",
      "│       │   ├── 7730.jpg\n",
      "│       │   ├── 7736.jpg\n",
      "│       │   ├── 7739.jpg\n",
      "│       │   ├── 7740.jpg\n",
      "│       │   ├── 7741.jpg\n",
      "│       │   ├── 7744.jpg\n",
      "│       │   ├── 7749.jpg\n",
      "│       │   ├── 7750.jpg\n",
      "│       │   ├── 7752.jpg\n",
      "│       │   ├── 7753.jpg\n",
      "│       │   ├── 7758.jpg\n",
      "│       │   ├── 7759.jpg\n",
      "│       │   ├── 7761.jpg\n",
      "│       │   ├── 7765.jpg\n",
      "│       │   ├── 7766.jpg\n",
      "│       │   ├── 7768.jpg\n",
      "│       │   ├── 7769.jpg\n",
      "│       │   ├── 776.jpg\n",
      "│       │   ├── 7770.jpg\n",
      "│       │   ├── 7772.jpg\n",
      "│       │   ├── 7774.jpg\n",
      "│       │   ├── 7775.jpg\n",
      "│       │   ├── 7777.jpg\n",
      "│       │   ├── 7780.jpg\n",
      "│       │   ├── 7781.jpg\n",
      "│       │   ├── 7788.jpg\n",
      "│       │   ├── 778.jpg\n",
      "│       │   ├── 7790.jpg\n",
      "│       │   ├── 7791.jpg\n",
      "│       │   ├── 7793.jpg\n",
      "│       │   ├── 7795.jpg\n",
      "│       │   ├── 7796.jpg\n",
      "│       │   ├── 7797.jpg\n",
      "│       │   ├── 7798.jpg\n",
      "│       │   ├── 7799.jpg\n",
      "│       │   ├── 77.jpg\n",
      "│       │   ├── 7800.jpg\n",
      "│       │   ├── 7802.jpg\n",
      "│       │   ├── 7803.jpg\n",
      "│       │   ├── 7804.jpg\n",
      "│       │   ├── 7806.jpg\n",
      "│       │   ├── 7808.jpg\n",
      "│       │   ├── 780.jpg\n",
      "│       │   ├── 7811.jpg\n",
      "│       │   ├── 7814.jpg\n",
      "│       │   ├── 7817.jpg\n",
      "│       │   ├── 7828.jpg\n",
      "│       │   ├── 7829.jpg\n",
      "│       │   ├── 7830.jpg\n",
      "│       │   ├── 7831.jpg\n",
      "│       │   ├── 7834.jpg\n",
      "│       │   ├── 7835.jpg\n",
      "│       │   ├── 7839.jpg\n",
      "│       │   ├── 783.jpg\n",
      "│       │   ├── 7841.jpg\n",
      "│       │   ├── 7842.jpg\n",
      "│       │   ├── 7845.jpg\n",
      "│       │   ├── 7846.jpg\n",
      "│       │   ├── 7847.jpg\n",
      "│       │   ├── 7849.jpg\n",
      "│       │   ├── 7851.jpg\n",
      "│       │   ├── 7854.jpg\n",
      "│       │   ├── 7855.jpg\n",
      "│       │   ├── 7856.jpg\n",
      "│       │   ├── 7857.jpg\n",
      "│       │   ├── 7858.jpg\n",
      "│       │   ├── 7859.jpg\n",
      "│       │   ├── 7862.jpg\n",
      "│       │   ├── 7864.jpg\n",
      "│       │   ├── 7868.jpg\n",
      "│       │   ├── 7869.jpg\n",
      "│       │   ├── 7870.jpg\n",
      "│       │   ├── 7872.jpg\n",
      "│       │   ├── 7877.jpg\n",
      "│       │   ├── 7878.jpg\n",
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      "│       │   ├── 788.jpg\n",
      "│       │   ├── 7892.jpg\n",
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      "│       │   ├── 7896.jpg\n",
      "│       │   ├── 7900.jpg\n",
      "│       │   ├── 7901.jpg\n",
      "│       │   ├── 7902.jpg\n",
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      "│       │   ├── 7904.jpg\n",
      "│       │   ├── 7906.jpg\n",
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      "│       │   ├── 7910.jpg\n",
      "│       │   ├── 7912.jpg\n",
      "│       │   ├── 7922.jpg\n",
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      "│       │   ├── 7925.jpg\n",
      "│       │   ├── 7926.jpg\n",
      "│       │   ├── 7927.jpg\n",
      "│       │   ├── 7930.jpg\n",
      "│       │   ├── 7933.jpg\n",
      "│       │   ├── 7935.jpg\n",
      "│       │   ├── 7937.jpg\n",
      "│       │   ├── 7939.jpg\n",
      "│       │   ├── 7940.jpg\n",
      "│       │   ├── 7942.jpg\n",
      "│       │   ├── 7943.jpg\n",
      "│       │   ├── 7945.jpg\n",
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      "│       │   ├── 7948.jpg\n",
      "│       │   ├── 7950.jpg\n",
      "│       │   ├── 7958.jpg\n",
      "│       │   ├── 795.jpg\n",
      "│       │   ├── 7960.jpg\n",
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      "│       │   ├── 7962.jpg\n",
      "│       │   ├── 7965.jpg\n",
      "│       │   ├── 7967.jpg\n",
      "│       │   ├── 796.jpg\n",
      "│       │   ├── 7977.jpg\n",
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      "│       │   ├── 7979.jpg\n",
      "│       │   ├── 797.jpg\n",
      "│       │   ├── 7980.jpg\n",
      "│       │   ├── 7983.jpg\n",
      "│       │   ├── 7987.jpg\n",
      "│       │   ├── 7988.jpg\n",
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      "│       │   ├── 7991.jpg\n",
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      "│       │   ├── 79.jpg\n",
      "│       │   ├── 8000.jpg\n",
      "│       │   ├── 8001.jpg\n",
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      "│       │   ├── 8004.jpg\n",
      "│       │   ├── 800.jpg\n",
      "│       │   ├── 8011.jpg\n",
      "│       │   ├── 8014.jpg\n",
      "│       │   ├── 8016.jpg\n",
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      "│       │   ├── 8023.jpg\n",
      "│       │   ├── 8026.jpg\n",
      "│       │   ├── 8028.jpg\n",
      "│       │   ├── 8033.jpg\n",
      "│       │   ├── 8036.jpg\n",
      "│       │   ├── 8037.jpg\n",
      "│       │   ├── 8041.jpg\n",
      "│       │   ├── 8043.jpg\n",
      "│       │   ├── 8047.jpg\n",
      "│       │   ├── 8048.jpg\n",
      "│       │   ├── 8049.jpg\n",
      "│       │   ├── 8050.jpg\n",
      "│       │   ├── 8054.jpg\n",
      "│       │   ├── 8055.jpg\n",
      "│       │   ├── 8058.jpg\n",
      "│       │   ├── 805.jpg\n",
      "│       │   ├── 8060.jpg\n",
      "│       │   ├── 8061.jpg\n",
      "│       │   ├── 8062.jpg\n",
      "│       │   ├── 8067.jpg\n",
      "│       │   ├── 8068.jpg\n",
      "│       │   ├── 8069.jpg\n",
      "│       │   ├── 806.jpg\n",
      "│       │   ├── 8070.jpg\n",
      "│       │   ├── 8071.jpg\n",
      "│       │   ├── 8072.jpg\n",
      "│       │   ├── 8073.jpg\n",
      "│       │   ├── 807.jpg\n",
      "│       │   ├── 8080.jpg\n",
      "│       │   ├── 8081.jpg\n",
      "│       │   ├── 8084.jpg\n",
      "│       │   ├── 8089.jpg\n",
      "│       │   ├── 808.jpg\n",
      "│       │   ├── 8092.jpg\n",
      "│       │   ├── 8094.jpg\n",
      "│       │   ├── 8095.jpg\n",
      "│       │   ├── 8097.jpg\n",
      "│       │   ├── 8098.jpg\n",
      "│       │   ├── 80.jpg\n",
      "│       │   ├── 8100.jpg\n",
      "│       │   ├── 8103.jpg\n",
      "│       │   ├── 8104.jpg\n",
      "│       │   ├── 8106.jpg\n",
      "│       │   ├── 8107.jpg\n",
      "│       │   ├── 8109.jpg\n",
      "│       │   ├── 810.jpg\n",
      "│       │   ├── 8111.jpg\n",
      "│       │   ├── 8113.jpg\n",
      "│       │   ├── 8114.jpg\n",
      "│       │   ├── 8120.jpg\n",
      "│       │   ├── 8121.jpg\n",
      "│       │   ├── 8125.jpg\n",
      "│       │   ├── 8127.jpg\n",
      "│       │   ├── 812.jpg\n",
      "│       │   ├── 8134.jpg\n",
      "│       │   ├── 8139.jpg\n",
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      "│       │   ├── 8144.jpg\n",
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      "│       │   ├── 814.jpg\n",
      "│       │   ├── 8151.jpg\n",
      "│       │   ├── 8155.jpg\n",
      "│       │   ├── 8157.jpg\n",
      "│       │   ├── 815.jpg\n",
      "│       │   ├── 8160.jpg\n",
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      "│       │   ├── 8164.jpg\n",
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      "│       │   ├── 8168.jpg\n",
      "│       │   ├── 8171.jpg\n",
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      "│       │   ├── 817.jpg\n",
      "│       │   ├── 8182.jpg\n",
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      "│       │   ├── 8202.jpg\n",
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      "│       │   ├── 820.jpg\n",
      "│       │   ├── 8210.jpg\n",
      "│       │   ├── 8211.jpg\n",
      "│       │   ├── 8212.jpg\n",
      "│       │   ├── 8216.jpg\n",
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      "│       │   ├── 8288.jpg\n",
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      "│       │   ├── 8300.jpg\n",
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      "│       │   ├── 8347.jpg\n",
      "│       │   ├── 8348.jpg\n",
      "│       │   ├── 8350.jpg\n",
      "│       │   ├── 8352.jpg\n",
      "│       │   ├── 8353.jpg\n",
      "│       │   ├── 8355.jpg\n",
      "│       │   ├── 8356.jpg\n",
      "│       │   ├── 8359.jpg\n",
      "│       │   ├── 835.jpg\n",
      "│       │   ├── 8362.jpg\n",
      "│       │   ├── 8363.jpg\n",
      "│       │   ├── 8364.jpg\n",
      "│       │   ├── 8368.jpg\n",
      "│       │   ├── 8369.jpg\n",
      "│       │   ├── 8371.jpg\n",
      "│       │   ├── 8372.jpg\n",
      "│       │   ├── 8373.jpg\n",
      "│       │   ├── 8374.jpg\n",
      "│       │   ├── 8376.jpg\n",
      "│       │   ├── 8379.jpg\n",
      "│       │   ├── 837.jpg\n",
      "│       │   ├── 8381.jpg\n",
      "│       │   ├── 8382.jpg\n",
      "│       │   ├── 8384.jpg\n",
      "│       │   ├── 8385.jpg\n",
      "│       │   ├── 8389.jpg\n",
      "│       │   ├── 8391.jpg\n",
      "│       │   ├── 8393.jpg\n",
      "│       │   ├── 8394.jpg\n",
      "│       │   ├── 8395.jpg\n",
      "│       │   ├── 8396.jpg\n",
      "│       │   ├── 8397.jpg\n",
      "│       │   ├── 83.jpg\n",
      "│       │   ├── 8402.jpg\n",
      "│       │   ├── 8404.jpg\n",
      "│       │   ├── 8405.jpg\n",
      "│       │   ├── 8407.jpg\n",
      "│       │   ├── 8408.jpg\n",
      "│       │   ├── 8409.jpg\n",
      "│       │   ├── 840.jpg\n",
      "│       │   ├── 8410.jpg\n",
      "│       │   ├── 8412.jpg\n",
      "│       │   ├── 8414.jpg\n",
      "│       │   ├── 8415.jpg\n",
      "│       │   ├── 8416.jpg\n",
      "│       │   ├── 8417.jpg\n",
      "│       │   ├── 8419.jpg\n",
      "│       │   ├── 841.jpg\n",
      "│       │   ├── 8420.jpg\n",
      "│       │   ├── 8422.jpg\n",
      "│       │   ├── 8424.jpg\n",
      "│       │   ├── 8425.jpg\n",
      "│       │   ├── 8426.jpg\n",
      "│       │   ├── 8427.jpg\n",
      "│       │   ├── 842.jpg\n",
      "│       │   ├── 8433.jpg\n",
      "│       │   ├── 8434.jpg\n",
      "│       │   ├── 8436.jpg\n",
      "│       │   ├── 8437.jpg\n",
      "│       │   ├── 843.jpg\n",
      "│       │   ├── 8441.jpg\n",
      "│       │   ├── 8443.jpg\n",
      "│       │   ├── 8445.jpg\n",
      "│       │   ├── 8446.jpg\n",
      "│       │   ├── 8447.jpg\n",
      "│       │   ├── 8449.jpg\n",
      "│       │   ├── 844.jpg\n",
      "│       │   ├── 8451.jpg\n",
      "│       │   ├── 8453.jpg\n",
      "│       │   ├── 8456.jpg\n",
      "│       │   ├── 8457.jpg\n",
      "│       │   ├── 8458.jpg\n",
      "│       │   ├── 8459.jpg\n",
      "│       │   ├── 8464.jpg\n",
      "│       │   ├── 8467.jpg\n",
      "│       │   ├── 8468.jpg\n",
      "│       │   ├── 8470.jpg\n",
      "│       │   ├── 8472.jpg\n",
      "│       │   ├── 8477.jpg\n",
      "│       │   ├── 8478.jpg\n",
      "│       │   ├── 8479.jpg\n",
      "│       │   ├── 8481.jpg\n",
      "│       │   ├── 8482.jpg\n",
      "│       │   ├── 8484.jpg\n",
      "│       │   ├── 8487.jpg\n",
      "│       │   ├── 8493.jpg\n",
      "│       │   ├── 8496.jpg\n",
      "│       │   ├── 8498.jpg\n",
      "│       │   ├── 8500.jpg\n",
      "│       │   ├── 8501.jpg\n",
      "│       │   ├── 8503.jpg\n",
      "│       │   ├── 8506.jpg\n",
      "│       │   ├── 8508.jpg\n",
      "│       │   ├── 850.jpg\n",
      "│       │   ├── 8510.jpg\n",
      "│       │   ├── 8511.jpg\n",
      "│       │   ├── 8512.jpg\n",
      "│       │   ├── 8513.jpg\n",
      "│       │   ├── 8516.jpg\n",
      "│       │   ├── 8517.jpg\n",
      "│       │   ├── 851.jpg\n",
      "│       │   ├── 8520.jpg\n",
      "│       │   ├── 8521.jpg\n",
      "│       │   ├── 8526.jpg\n",
      "│       │   ├── 8527.jpg\n",
      "│       │   ├── 8535.jpg\n",
      "│       │   ├── 8536.jpg\n",
      "│       │   ├── 8538.jpg\n",
      "│       │   ├── 853.jpg\n",
      "│       │   ├── 8540.jpg\n",
      "│       │   ├── 8543.jpg\n",
      "│       │   ├── 8545.jpg\n",
      "│       │   ├── 8546.jpg\n",
      "│       │   ├── 8548.jpg\n",
      "│       │   ├── 8549.jpg\n",
      "│       │   ├── 8550.jpg\n",
      "│       │   ├── 8553.jpg\n",
      "│       │   ├── 8554.jpg\n",
      "│       │   ├── 8555.jpg\n",
      "│       │   ├── 8556.jpg\n",
      "│       │   ├── 8557.jpg\n",
      "│       │   ├── 8559.jpg\n",
      "│       │   ├── 855.jpg\n",
      "│       │   ├── 8560.jpg\n",
      "│       │   ├── 8563.jpg\n",
      "│       │   ├── 8565.jpg\n",
      "│       │   ├── 8568.jpg\n",
      "│       │   ├── 8569.jpg\n",
      "│       │   ├── 8573.jpg\n",
      "│       │   ├── 8575.jpg\n",
      "│       │   ├── 8576.jpg\n",
      "│       │   ├── 857.jpg\n",
      "│       │   ├── 8581.jpg\n",
      "│       │   ├── 8583.jpg\n",
      "│       │   ├── 8586.jpg\n",
      "│       │   ├── 8589.jpg\n",
      "│       │   ├── 8590.jpg\n",
      "│       │   ├── 8591.jpg\n",
      "│       │   ├── 8592.jpg\n",
      "│       │   ├── 8595.jpg\n",
      "│       │   ├── 8596.jpg\n",
      "│       │   ├── 8597.jpg\n",
      "│       │   ├── 859.jpg\n",
      "│       │   ├── 85.jpg\n",
      "│       │   ├── 8601.jpg\n",
      "│       │   ├── 8602.jpg\n",
      "│       │   ├── 8606.jpg\n",
      "│       │   ├── 8607.jpg\n",
      "│       │   ├── 8609.jpg\n",
      "│       │   ├── 8611.jpg\n",
      "│       │   ├── 8612.jpg\n",
      "│       │   ├── 8614.jpg\n",
      "│       │   ├── 8615.jpg\n",
      "│       │   ├── 8616.jpg\n",
      "│       │   ├── 8619.jpg\n",
      "│       │   ├── 861.jpg\n",
      "│       │   ├── 8622.jpg\n",
      "│       │   ├── 8625.jpg\n",
      "│       │   ├── 8627.jpg\n",
      "│       │   ├── 8628.jpg\n",
      "│       │   ├── 8630.jpg\n",
      "│       │   ├── 8633.jpg\n",
      "│       │   ├── 8636.jpg\n",
      "│       │   ├── 8638.jpg\n",
      "│       │   ├── 8639.jpg\n",
      "│       │   ├── 863.jpg\n",
      "│       │   ├── 8641.jpg\n",
      "│       │   ├── 8643.jpg\n",
      "│       │   ├── 8645.jpg\n",
      "│       │   ├── 8646.jpg\n",
      "│       │   ├── 8647.jpg\n",
      "│       │   ├── 8648.jpg\n",
      "│       │   ├── 8651.jpg\n",
      "│       │   ├── 8652.jpg\n",
      "│       │   ├── 8653.jpg\n",
      "│       │   ├── 8654.jpg\n",
      "│       │   ├── 8657.jpg\n",
      "│       │   ├── 8659.jpg\n",
      "│       │   ├── 865.jpg\n",
      "│       │   ├── 8668.jpg\n",
      "│       │   ├── 8669.jpg\n",
      "│       │   ├── 8670.jpg\n",
      "│       │   ├── 8672.jpg\n",
      "│       │   ├── 8674.jpg\n",
      "│       │   ├── 8676.jpg\n",
      "│       │   ├── 8677.jpg\n",
      "│       │   ├── 8678.jpg\n",
      "│       │   ├── 8680.jpg\n",
      "│       │   ├── 8682.jpg\n",
      "│       │   ├── 8685.jpg\n",
      "│       │   ├── 8687.jpg\n",
      "│       │   ├── 8689.jpg\n",
      "│       │   ├── 868.jpg\n",
      "│       │   ├── 8691.jpg\n",
      "│       │   ├── 8693.jpg\n",
      "│       │   ├── 8694.jpg\n",
      "│       │   ├── 8695.jpg\n",
      "│       │   ├── 8698.jpg\n",
      "│       │   ├── 86.jpg\n",
      "│       │   ├── 8700.jpg\n",
      "│       │   ├── 8701.jpg\n",
      "│       │   ├── 8703.jpg\n",
      "│       │   ├── 8705.jpg\n",
      "│       │   ├── 8706.jpg\n",
      "│       │   ├── 8707.jpg\n",
      "│       │   ├── 8713.jpg\n",
      "│       │   ├── 8715.jpg\n",
      "│       │   ├── 8720.jpg\n",
      "│       │   ├── 8721.jpg\n",
      "│       │   ├── 8722.jpg\n",
      "│       │   ├── 8724.jpg\n",
      "│       │   ├── 8727.jpg\n",
      "│       │   ├── 8731.jpg\n",
      "│       │   ├── 8734.jpg\n",
      "│       │   ├── 8735.jpg\n",
      "│       │   ├── 8736.jpg\n",
      "│       │   ├── 8737.jpg\n",
      "│       │   ├── 873.jpg\n",
      "│       │   ├── 8741.jpg\n",
      "│       │   ├── 8743.jpg\n",
      "│       │   ├── 8744.jpg\n",
      "│       │   ├── 8746.jpg\n",
      "│       │   ├── 874.jpg\n",
      "│       │   ├── 8751.jpg\n",
      "│       │   ├── 8755.jpg\n",
      "│       │   ├── 875.jpg\n",
      "│       │   ├── 8762.jpg\n",
      "│       │   ├── 8763.jpg\n",
      "│       │   ├── 8766.jpg\n",
      "│       │   ├── 8767.jpg\n",
      "│       │   ├── 8772.jpg\n",
      "│       │   ├── 8780.jpg\n",
      "│       │   ├── 8781.jpg\n",
      "│       │   ├── 8782.jpg\n",
      "│       │   ├── 8783.jpg\n",
      "│       │   ├── 8784.jpg\n",
      "│       │   ├── 8785.jpg\n",
      "│       │   ├── 8788.jpg\n",
      "│       │   ├── 8789.jpg\n",
      "│       │   ├── 878.jpg\n",
      "│       │   ├── 8791.jpg\n",
      "│       │   ├── 8797.jpg\n",
      "│       │   ├── 8799.jpg\n",
      "│       │   ├── 879.jpg\n",
      "│       │   ├── 87.jpg\n",
      "│       │   ├── 8802.jpg\n",
      "│       │   ├── 8803.jpg\n",
      "│       │   ├── 8805.jpg\n",
      "│       │   ├── 8807.jpg\n",
      "│       │   ├── 880.jpg\n",
      "│       │   ├── 8810.jpg\n",
      "│       │   ├── 8816.jpg\n",
      "│       │   ├── 8819.jpg\n",
      "│       │   ├── 881.jpg\n",
      "│       │   ├── 8821.jpg\n",
      "│       │   ├── 8823.jpg\n",
      "│       │   ├── 8825.jpg\n",
      "│       │   ├── 8829.jpg\n",
      "│       │   ├── 8830.jpg\n",
      "│       │   ├── 8831.jpg\n",
      "│       │   ├── 8832.jpg\n",
      "│       │   ├── 8834.jpg\n",
      "│       │   ├── 8838.jpg\n",
      "│       │   ├── 883.jpg\n",
      "│       │   ├── 8844.jpg\n",
      "│       │   ├── 8845.jpg\n",
      "│       │   ├── 884.jpg\n",
      "│       │   ├── 8851.jpg\n",
      "│       │   ├── 8853.jpg\n",
      "│       │   ├── 8854.jpg\n",
      "│       │   ├── 8855.jpg\n",
      "│       │   ├── 8856.jpg\n",
      "│       │   ├── 8857.jpg\n",
      "│       │   ├── 8866.jpg\n",
      "│       │   ├── 8867.jpg\n",
      "│       │   ├── 8868.jpg\n",
      "│       │   ├── 886.jpg\n",
      "│       │   ├── 8871.jpg\n",
      "│       │   ├── 8872.jpg\n",
      "│       │   ├── 8873.jpg\n",
      "│       │   ├── 8874.jpg\n",
      "│       │   ├── 8876.jpg\n",
      "│       │   ├── 8877.jpg\n",
      "│       │   ├── 8879.jpg\n",
      "│       │   ├── 8883.jpg\n",
      "│       │   ├── 8885.jpg\n",
      "│       │   ├── 8889.jpg\n",
      "│       │   ├── 8891.jpg\n",
      "│       │   ├── 8894.jpg\n",
      "│       │   ├── 8897.jpg\n",
      "│       │   ├── 889.jpg\n",
      "│       │   ├── 8901.jpg\n",
      "│       │   ├── 8902.jpg\n",
      "│       │   ├── 8903.jpg\n",
      "│       │   ├── 8905.jpg\n",
      "│       │   ├── 8906.jpg\n",
      "│       │   ├── 8911.jpg\n",
      "│       │   ├── 8912.jpg\n",
      "│       │   ├── 8913.jpg\n",
      "│       │   ├── 8928.jpg\n",
      "│       │   ├── 892.jpg\n",
      "│       │   ├── 8934.jpg\n",
      "│       │   ├── 8937.jpg\n",
      "│       │   ├── 8938.jpg\n",
      "│       │   ├── 8940.jpg\n",
      "│       │   ├── 8941.jpg\n",
      "│       │   ├── 8948.jpg\n",
      "│       │   ├── 8949.jpg\n",
      "│       │   ├── 8950.jpg\n",
      "│       │   ├── 8951.jpg\n",
      "│       │   ├── 8952.jpg\n",
      "│       │   ├── 8953.jpg\n",
      "│       │   ├── 8955.jpg\n",
      "│       │   ├── 8956.jpg\n",
      "│       │   ├── 895.jpg\n",
      "│       │   ├── 8960.jpg\n",
      "│       │   ├── 8962.jpg\n",
      "│       │   ├── 8966.jpg\n",
      "│       │   ├── 8967.jpg\n",
      "│       │   ├── 8970.jpg\n",
      "│       │   ├── 8975.jpg\n",
      "│       │   ├── 8976.jpg\n",
      "│       │   ├── 8977.jpg\n",
      "│       │   ├── 8980.jpg\n",
      "│       │   ├── 8981.jpg\n",
      "│       │   ├── 8982.jpg\n",
      "│       │   ├── 8986.jpg\n",
      "│       │   ├── 8988.jpg\n",
      "│       │   ├── 8989.jpg\n",
      "│       │   ├── 898.jpg\n",
      "│       │   ├── 8990.jpg\n",
      "│       │   ├── 8993.jpg\n",
      "│       │   ├── 8996.jpg\n",
      "│       │   ├── 8997.jpg\n",
      "│       │   ├── 8998.jpg\n",
      "│       │   ├── 8.jpg\n",
      "│       │   ├── 9000.jpg\n",
      "│       │   ├── 9001.jpg\n",
      "│       │   ├── 9002.jpg\n",
      "│       │   ├── 9008.jpg\n",
      "│       │   ├── 900.jpg\n",
      "│       │   ├── 9010.jpg\n",
      "│       │   ├── 9011.jpg\n",
      "│       │   ├── 9014.jpg\n",
      "│       │   ├── 9015.jpg\n",
      "│       │   ├── 9017.jpg\n",
      "│       │   ├── 9019.jpg\n",
      "│       │   ├── 901.jpg\n",
      "│       │   ├── 9021.jpg\n",
      "│       │   ├── 9023.jpg\n",
      "│       │   ├── 9026.jpg\n",
      "│       │   ├── 9028.jpg\n",
      "│       │   ├── 9029.jpg\n",
      "│       │   ├── 902.jpg\n",
      "│       │   ├── 9030.jpg\n",
      "│       │   ├── 9031.jpg\n",
      "│       │   ├── 9032.jpg\n",
      "│       │   ├── 9034.jpg\n",
      "│       │   ├── 9035.jpg\n",
      "│       │   ├── 9037.jpg\n",
      "│       │   ├── 9039.jpg\n",
      "│       │   ├── 9041.jpg\n",
      "│       │   ├── 9043.jpg\n",
      "│       │   ├── 9044.jpg\n",
      "│       │   ├── 9046.jpg\n",
      "│       │   ├── 9048.jpg\n",
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      "│       │   ├── 905.jpg\n",
      "│       │   ├── 9062.jpg\n",
      "│       │   ├── 9064.jpg\n",
      "│       │   ├── 9071.jpg\n",
      "│       │   ├── 9073.jpg\n",
      "│       │   ├── 9075.jpg\n",
      "│       │   ├── 9076.jpg\n",
      "│       │   ├── 9077.jpg\n",
      "│       │   ├── 9079.jpg\n",
      "│       │   ├── 907.jpg\n",
      "│       │   ├── 9080.jpg\n",
      "│       │   ├── 9082.jpg\n",
      "│       │   ├── 9084.jpg\n",
      "│       │   ├── 9086.jpg\n",
      "│       │   ├── 9089.jpg\n",
      "│       │   ├── 908.jpg\n",
      "│       │   ├── 9090.jpg\n",
      "│       │   ├── 9093.jpg\n",
      "│       │   ├── 9096.jpg\n",
      "│       │   ├── 909.jpg\n",
      "│       │   ├── 9102.jpg\n",
      "│       │   ├── 9103.jpg\n",
      "│       │   ├── 9104.jpg\n",
      "│       │   ├── 9105.jpg\n",
      "│       │   ├── 9107.jpg\n",
      "│       │   ├── 910.jpg\n",
      "│       │   ├── 9111.jpg\n",
      "│       │   ├── 9113.jpg\n",
      "│       │   ├── 9117.jpg\n",
      "│       │   ├── 9122.jpg\n",
      "│       │   ├── 9124.jpg\n",
      "│       │   ├── 9126.jpg\n",
      "│       │   ├── 912.jpg\n",
      "│       │   ├── 9130.jpg\n",
      "│       │   ├── 9131.jpg\n",
      "│       │   ├── 9132.jpg\n",
      "│       │   ├── 9135.jpg\n",
      "│       │   ├── 9137.jpg\n",
      "│       │   ├── 9138.jpg\n",
      "│       │   ├── 9139.jpg\n",
      "│       │   ├── 9140.jpg\n",
      "│       │   ├── 9145.jpg\n",
      "│       │   ├── 9146.jpg\n",
      "│       │   ├── 9147.jpg\n",
      "│       │   ├── 9148.jpg\n",
      "│       │   ├── 9151.jpg\n",
      "│       │   ├── 9154.jpg\n",
      "│       │   ├── 9155.jpg\n",
      "│       │   ├── 9156.jpg\n",
      "│       │   ├── 9158.jpg\n",
      "│       │   ├── 9160.jpg\n",
      "│       │   ├── 9162.jpg\n",
      "│       │   ├── 9163.jpg\n",
      "│       │   ├── 9166.jpg\n",
      "│       │   ├── 9167.jpg\n",
      "│       │   ├── 9169.jpg\n",
      "│       │   ├── 916.jpg\n",
      "│       │   ├── 9170.jpg\n",
      "│       │   ├── 9173.jpg\n",
      "│       │   ├── 9174.jpg\n",
      "│       │   ├── 9176.jpg\n",
      "│       │   ├── 9177.jpg\n",
      "│       │   ├── 9178.jpg\n",
      "│       │   ├── 9179.jpg\n",
      "│       │   ├── 917.jpg\n",
      "│       │   ├── 9181.jpg\n",
      "│       │   ├── 9184.jpg\n",
      "│       │   ├── 9185.jpg\n",
      "│       │   ├── 9187.jpg\n",
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      "│       │   ├── 9193.jpg\n",
      "│       │   ├── 9195.jpg\n",
      "│       │   ├── 919.jpg\n",
      "│       │   ├── 91.jpg\n",
      "│       │   ├── 9200.jpg\n",
      "│       │   ├── 9203.jpg\n",
      "│       │   ├── 9204.jpg\n",
      "│       │   ├── 9206.jpg\n",
      "│       │   ├── 9208.jpg\n",
      "│       │   ├── 920.jpg\n",
      "│       │   ├── 9211.jpg\n",
      "│       │   ├── 9214.jpg\n",
      "│       │   ├── 9219.jpg\n",
      "│       │   ├── 921.jpg\n",
      "│       │   ├── 9220.jpg\n",
      "│       │   ├── 9227.jpg\n",
      "│       │   ├── 9231.jpg\n",
      "│       │   ├── 9233.jpg\n",
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      "│       │   ├── 9237.jpg\n",
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      "│       │   ├── 9239.jpg\n",
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      "│       │   ├── 9242.jpg\n",
      "│       │   ├── 9244.jpg\n",
      "│       │   ├── 9247.jpg\n",
      "│       │   ├── 9250.jpg\n",
      "│       │   ├── 9252.jpg\n",
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      "│       │   ├── 9255.jpg\n",
      "│       │   ├── 9256.jpg\n",
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      "│       │   ├── 9261.jpg\n",
      "│       │   ├── 9263.jpg\n",
      "│       │   ├── 9265.jpg\n",
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      "│       │   ├── 9267.jpg\n",
      "│       │   ├── 9268.jpg\n",
      "│       │   ├── 926.jpg\n",
      "│       │   ├── 9271.jpg\n",
      "│       │   ├── 9272.jpg\n",
      "│       │   ├── 9273.jpg\n",
      "│       │   ├── 9274.jpg\n",
      "│       │   ├── 9275.jpg\n",
      "│       │   ├── 9276.jpg\n",
      "│       │   ├── 9277.jpg\n",
      "│       │   ├── 9279.jpg\n",
      "│       │   ├── 9281.jpg\n",
      "│       │   ├── 9286.jpg\n",
      "│       │   ├── 9292.jpg\n",
      "│       │   ├── 9293.jpg\n",
      "│       │   ├── 9295.jpg\n",
      "│       │   ├── 9297.jpg\n",
      "│       │   ├── 929.jpg\n",
      "│       │   ├── 9300.jpg\n",
      "│       │   ├── 9301.jpg\n",
      "│       │   ├── 9303.jpg\n",
      "│       │   ├── 9307.jpg\n",
      "│       │   ├── 9309.jpg\n",
      "│       │   ├── 930.jpg\n",
      "│       │   ├── 9311.jpg\n",
      "│       │   ├── 9317.jpg\n",
      "│       │   ├── 9318.jpg\n",
      "│       │   ├── 9320.jpg\n",
      "│       │   ├── 9323.jpg\n",
      "│       │   ├── 9325.jpg\n",
      "│       │   ├── 9327.jpg\n",
      "│       │   ├── 9331.jpg\n",
      "│       │   ├── 9332.jpg\n",
      "│       │   ├── 9334.jpg\n",
      "│       │   ├── 9335.jpg\n",
      "│       │   ├── 9338.jpg\n",
      "│       │   ├── 9340.jpg\n",
      "│       │   ├── 9341.jpg\n",
      "│       │   ├── 9342.jpg\n",
      "│       │   ├── 9346.jpg\n",
      "│       │   ├── 9347.jpg\n",
      "│       │   ├── 9348.jpg\n",
      "│       │   ├── 9351.jpg\n",
      "│       │   ├── 9353.jpg\n",
      "│       │   ├── 9355.jpg\n",
      "│       │   ├── 9356.jpg\n",
      "│       │   ├── 9358.jpg\n",
      "│       │   ├── 9359.jpg\n",
      "│       │   ├── 9363.jpg\n",
      "│       │   ├── 9367.jpg\n",
      "│       │   ├── 9372.jpg\n",
      "│       │   ├── 9374.jpg\n",
      "│       │   ├── 9375.jpg\n",
      "│       │   ├── 9376.jpg\n",
      "│       │   ├── 9378.jpg\n",
      "│       │   ├── 9379.jpg\n",
      "│       │   ├── 937.jpg\n",
      "│       │   ├── 9381.jpg\n",
      "│       │   ├── 9383.jpg\n",
      "│       │   ├── 9384.jpg\n",
      "│       │   ├── 9385.jpg\n",
      "│       │   ├── 9387.jpg\n",
      "│       │   ├── 9388.jpg\n",
      "│       │   ├── 9390.jpg\n",
      "│       │   ├── 9392.jpg\n",
      "│       │   ├── 9394.jpg\n",
      "│       │   ├── 9396.jpg\n",
      "│       │   ├── 9401.jpg\n",
      "│       │   ├── 9402.jpg\n",
      "│       │   ├── 9403.jpg\n",
      "│       │   ├── 9404.jpg\n",
      "│       │   ├── 9406.jpg\n",
      "│       │   ├── 9409.jpg\n",
      "│       │   ├── 940.jpg\n",
      "│       │   ├── 9410.jpg\n",
      "│       │   ├── 9411.jpg\n",
      "│       │   ├── 9413.jpg\n",
      "│       │   ├── 9415.jpg\n",
      "│       │   ├── 9417.jpg\n",
      "│       │   ├── 9420.jpg\n",
      "│       │   ├── 9422.jpg\n",
      "│       │   ├── 9426.jpg\n",
      "│       │   ├── 9428.jpg\n",
      "│       │   ├── 9429.jpg\n",
      "│       │   ├── 9430.jpg\n",
      "│       │   ├── 9431.jpg\n",
      "│       │   ├── 9432.jpg\n",
      "│       │   ├── 9433.jpg\n",
      "│       │   ├── 9434.jpg\n",
      "│       │   ├── 9436.jpg\n",
      "│       │   ├── 9437.jpg\n",
      "│       │   ├── 9439.jpg\n",
      "│       │   ├── 943.jpg\n",
      "│       │   ├── 9440.jpg\n",
      "│       │   ├── 9443.jpg\n",
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      "│       │   ├── 9448.jpg\n",
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      "│       │   ├── 9451.jpg\n",
      "│       │   ├── 9454.jpg\n",
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      "│       │   ├── 9466.jpg\n",
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      "│       │   ├── 9469.jpg\n",
      "│       │   ├── 946.jpg\n",
      "│       │   ├── 9472.jpg\n",
      "│       │   ├── 9475.jpg\n",
      "│       │   ├── 9476.jpg\n",
      "│       │   ├── 9479.jpg\n",
      "│       │   ├── 9480.jpg\n",
      "│       │   ├── 9484.jpg\n",
      "│       │   ├── 9485.jpg\n",
      "│       │   ├── 9490.jpg\n",
      "│       │   ├── 9493.jpg\n",
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      "│       │   ├── 9497.jpg\n",
      "│       │   ├── 9499.jpg\n",
      "│       │   ├── 949.jpg\n",
      "│       │   ├── 9500.jpg\n",
      "│       │   ├── 9505.jpg\n",
      "│       │   ├── 9507.jpg\n",
      "│       │   ├── 9509.jpg\n",
      "│       │   ├── 9512.jpg\n",
      "│       │   ├── 9514.jpg\n",
      "│       │   ├── 9515.jpg\n",
      "│       │   ├── 9516.jpg\n",
      "│       │   ├── 9517.jpg\n",
      "│       │   ├── 9519.jpg\n",
      "│       │   ├── 9520.jpg\n",
      "│       │   ├── 9521.jpg\n",
      "│       │   ├── 9522.jpg\n",
      "│       │   ├── 9523.jpg\n",
      "│       │   ├── 9526.jpg\n",
      "│       │   ├── 9528.jpg\n",
      "│       │   ├── 952.jpg\n",
      "│       │   ├── 9533.jpg\n",
      "│       │   ├── 9535.jpg\n",
      "│       │   ├── 9536.jpg\n",
      "│       │   ├── 9541.jpg\n",
      "│       │   ├── 9542.jpg\n",
      "│       │   ├── 9543.jpg\n",
      "│       │   ├── 9545.jpg\n",
      "│       │   ├── 9548.jpg\n",
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      "│       │   ├── 9550.jpg\n",
      "│       │   ├── 9554.jpg\n",
      "│       │   ├── 9555.jpg\n",
      "│       │   ├── 9560.jpg\n",
      "│       │   ├── 9561.jpg\n",
      "│       │   ├── 9562.jpg\n",
      "│       │   ├── 9563.jpg\n",
      "│       │   ├── 9564.jpg\n",
      "│       │   ├── 9571.jpg\n",
      "│       │   ├── 9574.jpg\n",
      "│       │   ├── 9577.jpg\n",
      "│       │   ├── 957.jpg\n",
      "│       │   ├── 9580.jpg\n",
      "│       │   ├── 9581.jpg\n",
      "│       │   ├── 9583.jpg\n",
      "│       │   ├── 9585.jpg\n",
      "│       │   ├── 9586.jpg\n",
      "│       │   ├── 9588.jpg\n",
      "│       │   ├── 9590.jpg\n",
      "│       │   ├── 9592.jpg\n",
      "│       │   ├── 9593.jpg\n",
      "│       │   ├── 9596.jpg\n",
      "│       │   ├── 9598.jpg\n",
      "│       │   ├── 9600.jpg\n",
      "│       │   ├── 9601.jpg\n",
      "│       │   ├── 9602.jpg\n",
      "│       │   ├── 9605.jpg\n",
      "│       │   ├── 9606.jpg\n",
      "│       │   ├── 9607.jpg\n",
      "│       │   ├── 9608.jpg\n",
      "│       │   ├── 9610.jpg\n",
      "│       │   ├── 9612.jpg\n",
      "│       │   ├── 9613.jpg\n",
      "│       │   ├── 9614.jpg\n",
      "│       │   ├── 9615.jpg\n",
      "│       │   ├── 9616.jpg\n",
      "│       │   ├── 9622.jpg\n",
      "│       │   ├── 9624.jpg\n",
      "│       │   ├── 9625.jpg\n",
      "│       │   ├── 9626.jpg\n",
      "│       │   ├── 9627.jpg\n",
      "│       │   ├── 9629.jpg\n",
      "│       │   ├── 9631.jpg\n",
      "│       │   ├── 9632.jpg\n",
      "│       │   ├── 9639.jpg\n",
      "│       │   ├── 963.jpg\n",
      "│       │   ├── 9640.jpg\n",
      "│       │   ├── 9642.jpg\n",
      "│       │   ├── 9644.jpg\n",
      "│       │   ├── 9645.jpg\n",
      "│       │   ├── 9648.jpg\n",
      "│       │   ├── 9649.jpg\n",
      "│       │   ├── 9652.jpg\n",
      "│       │   ├── 9653.jpg\n",
      "│       │   ├── 9658.jpg\n",
      "│       │   ├── 9659.jpg\n",
      "│       │   ├── 966.jpg\n",
      "│       │   ├── 9670.jpg\n",
      "│       │   ├── 9671.jpg\n",
      "│       │   ├── 9674.jpg\n",
      "│       │   ├── 9675.jpg\n",
      "│       │   ├── 9677.jpg\n",
      "│       │   ├── 9678.jpg\n",
      "│       │   ├── 9679.jpg\n",
      "│       │   ├── 967.jpg\n",
      "│       │   ├── 9680.jpg\n",
      "│       │   ├── 9681.jpg\n",
      "│       │   ├── 9682.jpg\n",
      "│       │   ├── 9685.jpg\n",
      "│       │   ├── 9689.jpg\n",
      "│       │   ├── 9690.jpg\n",
      "│       │   ├── 9691.jpg\n",
      "│       │   ├── 9692.jpg\n",
      "│       │   ├── 9693.jpg\n",
      "│       │   ├── 9694.jpg\n",
      "│       │   ├── 9695.jpg\n",
      "│       │   ├── 9696.jpg\n",
      "│       │   ├── 969.jpg\n",
      "│       │   ├── 96.jpg\n",
      "│       │   ├── 9700.jpg\n",
      "│       │   ├── 9701.jpg\n",
      "│       │   ├── 9706.jpg\n",
      "│       │   ├── 9709.jpg\n",
      "│       │   ├── 970.jpg\n",
      "│       │   ├── 9710.jpg\n",
      "│       │   ├── 9711.jpg\n",
      "│       │   ├── 9712.jpg\n",
      "│       │   ├── 9713.jpg\n",
      "│       │   ├── 9715.jpg\n",
      "│       │   ├── 9716.jpg\n",
      "│       │   ├── 9717.jpg\n",
      "│       │   ├── 971.jpg\n",
      "│       │   ├── 9720.jpg\n",
      "│       │   ├── 9725.jpg\n",
      "│       │   ├── 9728.jpg\n",
      "│       │   ├── 9729.jpg\n",
      "│       │   ├── 9731.jpg\n",
      "│       │   ├── 9732.jpg\n",
      "│       │   ├── 9735.jpg\n",
      "│       │   ├── 9736.jpg\n",
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      "│       │   ├── 973.jpg\n",
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      "│       │   ├── 9751.jpg\n",
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      "│       │   ├── 9794.jpg\n",
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      "│       │   ├── 9802.jpg\n",
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      "│       │   ├── 980.jpg\n",
      "│       │   ├── 9810.jpg\n",
      "│       │   ├── 9811.jpg\n",
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      "│       │   ├── 981.jpg\n",
      "│       │   ├── 9820.jpg\n",
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      "│       │   ├── 9826.jpg\n",
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      "│       │   ├── 982.jpg\n",
      "│       │   ├── 9833.jpg\n",
      "│       │   ├── 9838.jpg\n",
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      "│       │   ├── 983.jpg\n",
      "│       │   ├── 9841.jpg\n",
      "│       │   ├── 9842.jpg\n",
      "│       │   ├── 9843.jpg\n",
      "│       │   ├── 9845.jpg\n",
      "│       │   ├── 9847.jpg\n",
      "│       │   ├── 9849.jpg\n",
      "│       │   ├── 984.jpg\n",
      "│       │   ├── 9850.jpg\n",
      "│       │   ├── 9851.jpg\n",
      "│       │   ├── 9852.jpg\n",
      "│       │   ├── 9857.jpg\n",
      "│       │   ├── 9861.jpg\n",
      "│       │   ├── 9865.jpg\n",
      "│       │   ├── 9866.jpg\n",
      "│       │   ├── 9867.jpg\n",
      "│       │   ├── 9868.jpg\n",
      "│       │   ├── 9869.jpg\n",
      "│       │   ├── 9870.jpg\n",
      "│       │   ├── 9873.jpg\n",
      "│       │   ├── 9875.jpg\n",
      "│       │   ├── 9879.jpg\n",
      "│       │   ├── 987.jpg\n",
      "│       │   ├── 9883.jpg\n",
      "│       │   ├── 9887.jpg\n",
      "│       │   ├── 9889.jpg\n",
      "│       │   ├── 9890.jpg\n",
      "│       │   ├── 9892.jpg\n",
      "│       │   ├── 9895.jpg\n",
      "│       │   ├── 9898.jpg\n",
      "│       │   ├── 98.jpg\n",
      "│       │   ├── 9903.jpg\n",
      "│       │   ├── 9907.jpg\n",
      "│       │   ├── 9908.jpg\n",
      "│       │   ├── 9909.jpg\n",
      "│       │   ├── 990.jpg\n",
      "│       │   ├── 9910.jpg\n",
      "│       │   ├── 9912.jpg\n",
      "│       │   ├── 9913.jpg\n",
      "│       │   ├── 9914.jpg\n",
      "│       │   ├── 9915.jpg\n",
      "│       │   ├── 9916.jpg\n",
      "│       │   ├── 9917.jpg\n",
      "│       │   ├── 9920.jpg\n",
      "│       │   ├── 9923.jpg\n",
      "│       │   ├── 9924.jpg\n",
      "│       │   ├── 9927.jpg\n",
      "│       │   ├── 9928.jpg\n",
      "│       │   ├── 9930.jpg\n",
      "│       │   ├── 9933.jpg\n",
      "│       │   ├── 9934.jpg\n",
      "│       │   ├── 9936.jpg\n",
      "│       │   ├── 9941.jpg\n",
      "│       │   ├── 9942.jpg\n",
      "│       │   ├── 9943.jpg\n",
      "│       │   ├── 9944.jpg\n",
      "│       │   ├── 9947.jpg\n",
      "│       │   ├── 9948.jpg\n",
      "│       │   ├── 9950.jpg\n",
      "│       │   ├── 9951.jpg\n",
      "│       │   ├── 9953.jpg\n",
      "│       │   ├── 9954.jpg\n",
      "│       │   ├── 9957.jpg\n",
      "│       │   ├── 9959.jpg\n",
      "│       │   ├── 9960.jpg\n",
      "│       │   ├── 9965.jpg\n",
      "│       │   ├── 9968.jpg\n",
      "│       │   ├── 9971.jpg\n",
      "│       │   ├── 9972.jpg\n",
      "│       │   ├── 9975.jpg\n",
      "│       │   ├── 997.jpg\n",
      "│       │   ├── 9980.jpg\n",
      "│       │   ├── 9983.jpg\n",
      "│       │   ├── 9984.jpg\n",
      "│       │   ├── 9985.jpg\n",
      "│       │   ├── 9988.jpg\n",
      "│       │   ├── 9989.jpg\n",
      "│       │   ├── 998.jpg\n",
      "│       │   ├── 9991.jpg\n",
      "│       │   ├── 9992.jpg\n",
      "│       │   ├── 9998.jpg\n",
      "│       │   └── 999.jpg\n",
      "│       ├── img_test.zip\n"
     ]
    }
   ],
   "source": [
    "!tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!unzip -q data/data80164/train_and_label.zip -d data/data80164\n",
    "!unzip -q data/data80164/img_test.zip -d data/data80164\n",
    "!tree data/data80164 -d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!ls \"./data/data80164/img_train/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!ls \"./data/data80164/lab_train/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-27T14:33:06.065627Z",
     "iopub.status.busy": "2022-02-27T14:33:06.064726Z",
     "iopub.status.idle": "2022-02-27T14:33:06.093580Z",
     "shell.execute_reply": "2022-02-27T14:33:06.092908Z",
     "shell.execute_reply.started": "2022-02-27T14:33:06.065586Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "img1_shape: (3, 256, 256)\n",
      "img1[:10, 0, 0]: [0 0 0]\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "\n",
    "img1_path = \"./data/data80164/img_train/T000000.jpg\"\n",
    "img1 = cv2.imread(img1_path)\n",
    "img1 = img1.transpose((2, 1, 0))  # 转换为H,W,C\n",
    "# 打印形状\n",
    "print('img1_shape:', img1.shape)\n",
    "print('img1[:10, 0, 0]:', img1[:10, 0, 0])\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-27T14:33:07.624870Z",
     "iopub.status.busy": "2022-02-27T14:33:07.624027Z",
     "iopub.status.idle": "2022-02-27T14:33:07.882286Z",
     "shell.execute_reply": "2022-02-27T14:33:07.881717Z",
     "shell.execute_reply.started": "2022-02-27T14:33:07.624832Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# 显示图像\n",
    "def show_img(img):\n",
    "    cv2.normalize(img, img, 0, 255, cv2.NORM_MINMAX)  # 归一化到0-255\n",
    "    img += 50  # 增加亮度看起来方便点\n",
    "    img[img > 255] = 255  # 避免超出255\n",
    "    return img\n",
    "img1_path = \"./data/data80164/img_train/T000000.jpg\"\n",
    "img1 = cv2.imread(img1_path)\n",
    "# 显示图像\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.subplot(231);\n",
    "plt.imshow(img1);\n",
    "plt.title('img1_cv2')  # cv2读取为BGR\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "DATA_ROOT_DIR = '/home/aistudio/data/data80164'\n",
    "\n",
    "def make_list():\n",
    "    img_list = [img.split('.')[0] for img in os.listdir(os.path.join(DATA_ROOT_DIR, 'img_train'))]\n",
    "    data_path_list = []\n",
    "    for image_id in img_list:\n",
    "        image_path = os.path.join(DATA_ROOT_DIR, 'img_train', f\"{image_id}.jpg\")\n",
    "        label_path = os.path.join(DATA_ROOT_DIR, 'lab_train', f\"{image_id}.png\")\n",
    "        data_path_list.append((image_path, label_path))\n",
    "        if not os.path.exists(image_path) or not os.path.exists(label_path):\n",
    "            print(f\"invalid path {image_path}\")\n",
    "    \n",
    "    np.random.seed(5)\n",
    "    np.random.shuffle(data_path_list)\n",
    "    total_len = len(data_path_list)\n",
    "    train_data_len = int(total_len*0.95)\n",
    "    train_data = data_path_list[0 : train_data_len]\n",
    "    val_data = data_path_list[train_data_len : ]\n",
    "\n",
    "    with open(os.path.join(DATA_ROOT_DIR, 'train_list.txt'), \"w\") as f:\n",
    "        for image, label in train_data:\n",
    "            f.write(f\"{image} {label}\\n\")\n",
    "\n",
    "    with open(os.path.join(DATA_ROOT_DIR, 'val_list.txt'), \"w\") as f:\n",
    "        for image, label in val_data:\n",
    "            f.write(f\"{image} {label}\\n\")\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    make_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "##下载安装paddlesegv2.1版本\n",
    "!git clone -b release/2.1 https://gitee.com/paddlepaddle/PaddleSeg.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-27T13:17:43.070331Z",
     "iopub.status.busy": "2022-02-27T13:17:43.070106Z",
     "iopub.status.idle": "2022-02-27T14:22:14.819403Z",
     "shell.execute_reply": "2022-02-27T14:22:14.818650Z",
     "shell.execute_reply.started": "2022-02-27T13:17:43.070304Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-02-27 21:17:44 [INFO]\t\n",
      "------------Environment Information-------------\n",
      "platform: Linux-4.4.0-150-generic-x86_64-with-debian-stretch-sid\n",
      "Python: 3.7.4 (default, Aug 13 2019, 20:35:49) [GCC 7.3.0]\n",
      "Paddle compiled with cuda: True\n",
      "NVCC: Cuda compilation tools, release 10.1, V10.1.243\n",
      "cudnn: 7.6\n",
      "GPUs used: 1\n",
      "CUDA_VISIBLE_DEVICES: None\n",
      "GPU: ['GPU 0: Tesla V100-SXM2-32GB']\n",
      "GCC: gcc (Ubuntu 7.5.0-3ubuntu1~16.04) 7.5.0\n",
      "PaddlePaddle: 2.2.2\n",
      "OpenCV: 4.1.1\n",
      "------------------------------------------------\n",
      "/home/aistudio/work/data/train_list.txt\n",
      "mode train /home/aistudio/work/data/train_list.txt /home/aistudio/work/data\n",
      "Total data for train : 63319\n",
      "mode val /home/aistudio/work/data/val_list.txt /home/aistudio/work/data\n",
      "Total data for val : 3333\n",
      "/home/aistudio/work/data/train_list.txt\n",
      "mode train /home/aistudio/work/data/train_list.txt /home/aistudio/work/data\n",
      "Total data for train : 63319\n",
      "/home/aistudio/work/data/train_list.txt\n",
      "mode train /home/aistudio/work/data/train_list.txt /home/aistudio/work/data\n",
      "Total data for train : 63319\n",
      "/home/aistudio/work/data/train_list.txt\n",
      "mode train /home/aistudio/work/data/train_list.txt /home/aistudio/work/data\n",
      "Total data for train : 63319\n",
      "2022-02-27 21:17:45 [INFO]\t\n",
      "---------------Config Information---------------\n",
      "batch_size: 12\n",
      "iters: 150000\n",
      "loss:\n",
      "  coef:\n",
      "  - 1\n",
      "  - 0.4\n",
      "  types:\n",
      "  - ignore_index: 255\n",
      "    type: CrossEntropyLoss\n",
      "lr_scheduler:\n",
      "  end_lr: 0\n",
      "  learning_rate: 0.01\n",
      "  power: 0.9\n",
      "  type: PolynomialDecay\n",
      "model:\n",
      "  backbone:\n",
      "    align_corners: false\n",
      "    pretrained: https://bj.bcebos.com/paddleseg/dygraph/hrnet_w48_ssld.tar.gz\n",
      "    type: HRNet_W48\n",
      "  backbone_indices:\n",
      "  - -1\n",
      "  pretrained: https://bj.bcebos.com/paddleseg/dygraph/ccf/fcn_hrnetw48_rs_256x256_160k/model.pdparams\n",
      "  type: OCRNet\n",
      "optimizer:\n",
      "  momentum: 0.9\n",
      "  type: sgd\n",
      "  weight_decay: 4.0e-05\n",
      "train_dataset:\n",
      "  mode: train\n",
      "  negetive_ratio: 0\n",
      "  train_dataset_root: /home/aistudio/work/data\n",
      "  transforms:\n",
      "  - max_scale_factor: 1.5\n",
      "    min_scale_factor: 0.75\n",
      "    scale_step_size: 0.25\n",
      "    type: ResizeStepScaling\n",
      "  - type: RandomHorizontalFlip\n",
      "  - type: RandomVerticalFlip\n",
      "  - max_rotation: 30\n",
      "    type: RandomRotation\n",
      "  - brightness_range: 0.2\n",
      "    contrast_range: 0.2\n",
      "    saturation_range: 0.2\n",
      "    type: RandomDistort\n",
      "  - type: Normalize\n",
      "  - target_size:\n",
      "    - 256\n",
      "    - 256\n",
      "    type: Resize\n",
      "  type: RemoteSensing\n",
      "val_dataset:\n",
      "  mode: val\n",
      "  train_dataset_root: /home/aistudio/work/data\n",
      "  transforms:\n",
      "  - target_size:\n",
      "    - 256\n",
      "    - 256\n",
      "    type: Resize\n",
      "  - type: Normalize\n",
      "  type: RemoteSensing\n",
      "------------------------------------------------\n",
      "/home/aistudio/work/data/train_list.txt\n",
      "mode train /home/aistudio/work/data/train_list.txt /home/aistudio/work/data\n",
      "Total data for train : 63319\n",
      "W0227 21:17:45.871143  3517 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1\n",
      "W0227 21:17:45.871203  3517 device_context.cc:465] device: 0, cuDNN Version: 7.6.\n",
      "2022-02-27 21:17:50 [INFO]\tLoading pretrained model from https://bj.bcebos.com/paddleseg/dygraph/hrnet_w48_ssld.tar.gz\n",
      "2022-02-27 21:17:53 [INFO]\tThere are 1525/1525 variables loaded into HRNet.\n",
      "2022-02-27 21:17:53 [INFO]\tLoading pretrained model from https://bj.bcebos.com/paddleseg/dygraph/ccf/fcn_hrnetw48_rs_256x256_160k/model.pdparams\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_pixel.0._conv.weight is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_pixel.0._conv.bias is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_pixel.0._batch_norm.weight is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_pixel.0._batch_norm.bias is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_pixel.0._batch_norm._mean is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_pixel.0._batch_norm._variance is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_pixel.1._conv.weight is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_pixel.1._conv.bias is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_pixel.1._batch_norm.weight is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_pixel.1._batch_norm.bias is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_pixel.1._batch_norm._mean is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_pixel.1._batch_norm._variance is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_object.0._conv.weight is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_object.0._conv.bias is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_object.0._batch_norm.weight is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_object.0._batch_norm.bias is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_object.0._batch_norm._mean is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_object.0._batch_norm._variance is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_object.1._conv.weight is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_object.1._conv.bias is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_object.1._batch_norm.weight is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_object.1._batch_norm.bias is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_object.1._batch_norm._mean is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_object.1._batch_norm._variance is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_down._conv.weight is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_down._conv.bias is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_down._batch_norm.weight is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_down._batch_norm.bias is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_down._batch_norm._mean is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_down._batch_norm._variance is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_up._conv.weight is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_up._conv.bias is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_up._batch_norm.weight is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_up._batch_norm.bias is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_up._batch_norm._mean is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.attention_block.f_up._batch_norm._variance is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.conv1x1.0._conv.weight is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.conv1x1.0._conv.bias is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.conv1x1.0._batch_norm.weight is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.conv1x1.0._batch_norm.bias is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.conv1x1.0._batch_norm._mean is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.spatial_ocr.conv1x1.0._batch_norm._variance is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.conv3x3_ocr._conv.weight is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.conv3x3_ocr._conv.bias is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.conv3x3_ocr._batch_norm.weight is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.conv3x3_ocr._batch_norm.bias is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.conv3x3_ocr._batch_norm._mean is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.conv3x3_ocr._batch_norm._variance is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.cls_head.weight is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.cls_head.bias is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.aux_head.0._conv.weight is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.aux_head.0._conv.bias is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.aux_head.0._batch_norm.weight is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.aux_head.0._batch_norm.bias is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.aux_head.0._batch_norm._mean is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.aux_head.0._batch_norm._variance is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.aux_head.1.weight is not in pretrained model\n",
      "2022-02-27 21:17:55 [WARNING]\thead.aux_head.1.bias is not in pretrained model\n",
      "2022-02-27 21:17:56 [INFO]\tThere are 1525/1583 variables loaded into OCRNet.\n",
      "2022-02-27 21:18:00 [INFO]\t[TRAIN] epoch: 1, iter: 10/150000, loss: 1.6045, lr: 0.009999, batch_cost: 0.4727, reader_cost: 0.03185, ips: 25.3877 samples/sec | ETA 19:41:35\n",
      "2022-02-27 21:18:05 [INFO]\t[TRAIN] epoch: 1, iter: 20/150000, loss: 2.1172, lr: 0.009999, batch_cost: 0.4176, reader_cost: 0.00014, ips: 28.7360 samples/sec | ETA 17:23:50\n",
      "2022-02-27 21:18:09 [INFO]\t[TRAIN] epoch: 1, iter: 30/150000, loss: 2.1201, lr: 0.009998, batch_cost: 0.4147, reader_cost: 0.00013, ips: 28.9399 samples/sec | ETA 17:16:25\n",
      "2022-02-27 21:18:13 [INFO]\t[TRAIN] epoch: 1, iter: 40/150000, loss: 1.8949, lr: 0.009998, batch_cost: 0.4201, reader_cost: 0.00014, ips: 28.5647 samples/sec | ETA 17:29:57\n",
      "2022-02-27 21:18:17 [INFO]\t[TRAIN] epoch: 1, iter: 50/150000, loss: 2.0109, lr: 0.009997, batch_cost: 0.4184, reader_cost: 0.00014, ips: 28.6778 samples/sec | ETA 17:25:45\n",
      "2022-02-27 21:18:21 [INFO]\t[TRAIN] epoch: 1, iter: 60/150000, loss: 1.8182, lr: 0.009996, batch_cost: 0.4195, reader_cost: 0.00013, ips: 28.6087 samples/sec | ETA 17:28:12\n",
      "2022-02-27 21:18:26 [INFO]\t[TRAIN] epoch: 1, iter: 70/150000, loss: 1.7730, lr: 0.009996, batch_cost: 0.4184, reader_cost: 0.00013, ips: 28.6820 samples/sec | ETA 17:25:27\n",
      "2022-02-27 21:18:30 [INFO]\t[TRAIN] epoch: 1, iter: 80/150000, loss: 1.8680, lr: 0.009995, batch_cost: 0.4147, reader_cost: 0.00014, ips: 28.9336 samples/sec | ETA 17:16:18\n",
      "2022-02-27 21:18:34 [INFO]\t[TRAIN] epoch: 1, iter: 90/150000, loss: 1.6064, lr: 0.009995, batch_cost: 0.4169, reader_cost: 0.00013, ips: 28.7840 samples/sec | ETA 17:21:37\n",
      "2022-02-27 21:18:38 [INFO]\t[TRAIN] epoch: 1, iter: 100/150000, loss: 1.9776, lr: 0.009994, batch_cost: 0.4185, reader_cost: 0.00014, ips: 28.6717 samples/sec | ETA 17:25:37\n",
      "2022-02-27 21:18:42 [INFO]\t[TRAIN] epoch: 1, iter: 110/150000, loss: 1.7538, lr: 0.009993, batch_cost: 0.4271, reader_cost: 0.00014, ips: 28.0940 samples/sec | ETA 17:47:03\n",
      "2022-02-27 21:18:47 [INFO]\t[TRAIN] epoch: 1, iter: 120/150000, loss: 1.7088, lr: 0.009993, batch_cost: 0.4175, reader_cost: 0.00013, ips: 28.7396 samples/sec | ETA 17:23:01\n",
      "2022-02-27 21:18:51 [INFO]\t[TRAIN] epoch: 1, iter: 130/150000, loss: 1.7125, lr: 0.009992, batch_cost: 0.4210, reader_cost: 0.00014, ips: 28.5053 samples/sec | ETA 17:31:31\n",
      "2022-02-27 21:18:55 [INFO]\t[TRAIN] epoch: 1, iter: 140/150000, loss: 1.5271, lr: 0.009992, batch_cost: 0.4159, reader_cost: 0.00013, ips: 28.8520 samples/sec | ETA 17:18:49\n",
      "2022-02-27 21:18:59 [INFO]\t[TRAIN] epoch: 1, iter: 150/150000, loss: 1.6882, lr: 0.009991, batch_cost: 0.4123, reader_cost: 0.00013, ips: 29.1073 samples/sec | ETA 17:09:38\n",
      "2022-02-27 21:19:03 [INFO]\t[TRAIN] epoch: 1, iter: 160/150000, loss: 1.8246, lr: 0.009990, batch_cost: 0.4115, reader_cost: 0.00013, ips: 29.1618 samples/sec | ETA 17:07:38\n",
      "2022-02-27 21:19:07 [INFO]\t[TRAIN] epoch: 1, iter: 170/150000, loss: 1.8306, lr: 0.009990, batch_cost: 0.4154, reader_cost: 0.00013, ips: 28.8870 samples/sec | ETA 17:17:21\n",
      "2022-02-27 21:19:11 [INFO]\t[TRAIN] epoch: 1, iter: 180/150000, loss: 1.6670, lr: 0.009989, batch_cost: 0.4132, reader_cost: 0.00013, ips: 29.0423 samples/sec | ETA 17:11:44\n",
      "2022-02-27 21:19:16 [INFO]\t[TRAIN] epoch: 1, iter: 190/150000, loss: 1.5544, lr: 0.009989, batch_cost: 0.4257, reader_cost: 0.00014, ips: 28.1870 samples/sec | ETA 17:42:58\n",
      "2022-02-27 21:19:20 [INFO]\t[TRAIN] epoch: 1, iter: 200/150000, loss: 1.6415, lr: 0.009988, batch_cost: 0.4157, reader_cost: 0.00013, ips: 28.8692 samples/sec | ETA 17:17:46\n",
      "2022-02-27 21:19:24 [INFO]\t[TRAIN] epoch: 1, iter: 210/150000, loss: 1.6921, lr: 0.009987, batch_cost: 0.4190, reader_cost: 0.00014, ips: 28.6372 samples/sec | ETA 17:26:07\n",
      "2022-02-27 21:19:28 [INFO]\t[TRAIN] epoch: 1, iter: 220/150000, loss: 1.6997, lr: 0.009987, batch_cost: 0.4227, reader_cost: 0.00013, ips: 28.3888 samples/sec | ETA 17:35:12\n",
      "2022-02-27 21:19:32 [INFO]\t[TRAIN] epoch: 1, iter: 230/150000, loss: 1.5547, lr: 0.009986, batch_cost: 0.4207, reader_cost: 0.00013, ips: 28.5215 samples/sec | ETA 17:30:13\n",
      "2022-02-27 21:19:37 [INFO]\t[TRAIN] epoch: 1, iter: 240/150000, loss: 1.6866, lr: 0.009986, batch_cost: 0.4187, reader_cost: 0.00014, ips: 28.6593 samples/sec | ETA 17:25:06\n",
      "2022-02-27 21:19:41 [INFO]\t[TRAIN] epoch: 1, iter: 250/150000, loss: 1.6427, lr: 0.009985, batch_cost: 0.4481, reader_cost: 0.00015, ips: 26.7824 samples/sec | ETA 18:38:16\n",
      "2022-02-27 21:19:45 [INFO]\t[TRAIN] epoch: 1, iter: 260/150000, loss: 1.6843, lr: 0.009984, batch_cost: 0.4359, reader_cost: 0.00015, ips: 27.5277 samples/sec | ETA 18:07:55\n",
      "2022-02-27 21:19:50 [INFO]\t[TRAIN] epoch: 1, iter: 270/150000, loss: 1.6861, lr: 0.009984, batch_cost: 0.4300, reader_cost: 0.00013, ips: 27.9053 samples/sec | ETA 17:53:07\n",
      "2022-02-27 21:19:54 [INFO]\t[TRAIN] epoch: 1, iter: 280/150000, loss: 1.6487, lr: 0.009983, batch_cost: 0.4402, reader_cost: 0.00016, ips: 27.2619 samples/sec | ETA 18:18:23\n",
      "2022-02-27 21:19:58 [INFO]\t[TRAIN] epoch: 1, iter: 290/150000, loss: 1.6879, lr: 0.009983, batch_cost: 0.4225, reader_cost: 0.00013, ips: 28.3996 samples/sec | ETA 17:34:18\n",
      "2022-02-27 21:20:03 [INFO]\t[TRAIN] epoch: 1, iter: 300/150000, loss: 1.5570, lr: 0.009982, batch_cost: 0.4346, reader_cost: 0.00015, ips: 27.6096 samples/sec | ETA 18:04:24\n",
      "2022-02-27 21:20:07 [INFO]\t[TRAIN] epoch: 1, iter: 310/150000, loss: 1.5790, lr: 0.009981, batch_cost: 0.4311, reader_cost: 0.00014, ips: 27.8335 samples/sec | ETA 17:55:36\n",
      "2022-02-27 21:20:11 [INFO]\t[TRAIN] epoch: 1, iter: 320/150000, loss: 1.3773, lr: 0.009981, batch_cost: 0.4142, reader_cost: 0.00013, ips: 28.9710 samples/sec | ETA 17:13:18\n",
      "2022-02-27 21:20:15 [INFO]\t[TRAIN] epoch: 1, iter: 330/150000, loss: 1.6066, lr: 0.009980, batch_cost: 0.4193, reader_cost: 0.00013, ips: 28.6194 samples/sec | ETA 17:25:55\n",
      "2022-02-27 21:20:20 [INFO]\t[TRAIN] epoch: 1, iter: 340/150000, loss: 1.3154, lr: 0.009980, batch_cost: 0.4254, reader_cost: 0.00014, ips: 28.2116 samples/sec | ETA 17:40:58\n",
      "2022-02-27 21:20:24 [INFO]\t[TRAIN] epoch: 1, iter: 350/150000, loss: 1.7084, lr: 0.009979, batch_cost: 0.4275, reader_cost: 0.00013, ips: 28.0719 samples/sec | ETA 17:46:11\n",
      "2022-02-27 21:20:28 [INFO]\t[TRAIN] epoch: 1, iter: 360/150000, loss: 1.7649, lr: 0.009978, batch_cost: 0.4321, reader_cost: 0.00015, ips: 27.7698 samples/sec | ETA 17:57:42\n",
      "2022-02-27 21:20:32 [INFO]\t[TRAIN] epoch: 1, iter: 370/150000, loss: 1.7118, lr: 0.009978, batch_cost: 0.4172, reader_cost: 0.00013, ips: 28.7631 samples/sec | ETA 17:20:25\n",
      "2022-02-27 21:20:37 [INFO]\t[TRAIN] epoch: 1, iter: 380/150000, loss: 1.6460, lr: 0.009977, batch_cost: 0.4455, reader_cost: 0.00015, ips: 26.9374 samples/sec | ETA 18:30:52\n",
      "2022-02-27 21:20:41 [INFO]\t[TRAIN] epoch: 1, iter: 390/150000, loss: 1.5268, lr: 0.009977, batch_cost: 0.4364, reader_cost: 0.00135, ips: 27.4995 samples/sec | ETA 18:08:05\n",
      "2022-02-27 21:20:45 [INFO]\t[TRAIN] epoch: 1, iter: 400/150000, loss: 1.6565, lr: 0.009976, batch_cost: 0.4219, reader_cost: 0.00014, ips: 28.4438 samples/sec | ETA 17:31:53\n",
      "2022-02-27 21:20:50 [INFO]\t[TRAIN] epoch: 1, iter: 410/150000, loss: 1.5029, lr: 0.009975, batch_cost: 0.4207, reader_cost: 0.00014, ips: 28.5206 samples/sec | ETA 17:28:59\n",
      "2022-02-27 21:20:54 [INFO]\t[TRAIN] epoch: 1, iter: 420/150000, loss: 1.5535, lr: 0.009975, batch_cost: 0.4248, reader_cost: 0.00014, ips: 28.2495 samples/sec | ETA 17:38:59\n",
      "2022-02-27 21:20:58 [INFO]\t[TRAIN] epoch: 1, iter: 430/150000, loss: 1.7685, lr: 0.009974, batch_cost: 0.4249, reader_cost: 0.00014, ips: 28.2442 samples/sec | ETA 17:39:07\n",
      "2022-02-27 21:21:02 [INFO]\t[TRAIN] epoch: 1, iter: 440/150000, loss: 1.4337, lr: 0.009974, batch_cost: 0.4135, reader_cost: 0.00013, ips: 29.0182 samples/sec | ETA 17:10:48\n",
      "2022-02-27 21:21:07 [INFO]\t[TRAIN] epoch: 1, iter: 450/150000, loss: 1.5225, lr: 0.009973, batch_cost: 0.4214, reader_cost: 0.00013, ips: 28.4774 samples/sec | ETA 17:30:18\n",
      "2022-02-27 21:21:11 [INFO]\t[TRAIN] epoch: 1, iter: 460/150000, loss: 1.7867, lr: 0.009972, batch_cost: 0.4623, reader_cost: 0.00013, ips: 25.9574 samples/sec | ETA 19:12:11\n",
      "2022-02-27 21:21:15 [INFO]\t[TRAIN] epoch: 1, iter: 470/150000, loss: 1.7745, lr: 0.009972, batch_cost: 0.4229, reader_cost: 0.00013, ips: 28.3737 samples/sec | ETA 17:34:00\n",
      "2022-02-27 21:21:20 [INFO]\t[TRAIN] epoch: 1, iter: 480/150000, loss: 1.6096, lr: 0.009971, batch_cost: 0.4253, reader_cost: 0.00014, ips: 28.2163 samples/sec | ETA 17:39:48\n",
      "2022-02-27 21:21:24 [INFO]\t[TRAIN] epoch: 1, iter: 490/150000, loss: 1.6975, lr: 0.009971, batch_cost: 0.4278, reader_cost: 0.00013, ips: 28.0530 samples/sec | ETA 17:45:54\n",
      "2022-02-27 21:21:28 [INFO]\t[TRAIN] epoch: 1, iter: 500/150000, loss: 1.4770, lr: 0.009970, batch_cost: 0.4338, reader_cost: 0.00013, ips: 27.6645 samples/sec | ETA 18:00:48\n",
      "2022-02-27 21:21:33 [INFO]\t[TRAIN] epoch: 1, iter: 510/150000, loss: 1.6821, lr: 0.009969, batch_cost: 0.4311, reader_cost: 0.00013, ips: 27.8328 samples/sec | ETA 17:54:11\n",
      "2022-02-27 21:21:37 [INFO]\t[TRAIN] epoch: 1, iter: 520/150000, loss: 1.6539, lr: 0.009969, batch_cost: 0.4377, reader_cost: 0.00014, ips: 27.4167 samples/sec | ETA 18:10:25\n",
      "2022-02-27 21:21:42 [INFO]\t[TRAIN] epoch: 1, iter: 530/150000, loss: 1.6027, lr: 0.009968, batch_cost: 0.4762, reader_cost: 0.00015, ips: 25.1999 samples/sec | ETA 19:46:16\n",
      "2022-02-27 21:21:46 [INFO]\t[TRAIN] epoch: 1, iter: 540/150000, loss: 1.3280, lr: 0.009968, batch_cost: 0.4232, reader_cost: 0.00014, ips: 28.3562 samples/sec | ETA 17:34:09\n",
      "2022-02-27 21:21:51 [INFO]\t[TRAIN] epoch: 1, iter: 550/150000, loss: 1.7598, lr: 0.009967, batch_cost: 0.4679, reader_cost: 0.00014, ips: 25.6473 samples/sec | ETA 19:25:25\n",
      "2022-02-27 21:21:55 [INFO]\t[TRAIN] epoch: 1, iter: 560/150000, loss: 1.5943, lr: 0.009966, batch_cost: 0.4151, reader_cost: 0.00013, ips: 28.9108 samples/sec | ETA 17:13:48\n",
      "2022-02-27 21:21:59 [INFO]\t[TRAIN] epoch: 1, iter: 570/150000, loss: 1.4671, lr: 0.009966, batch_cost: 0.4230, reader_cost: 0.00014, ips: 28.3693 samples/sec | ETA 17:33:27\n",
      "2022-02-27 21:22:03 [INFO]\t[TRAIN] epoch: 1, iter: 580/150000, loss: 1.4352, lr: 0.009965, batch_cost: 0.4196, reader_cost: 0.00013, ips: 28.6000 samples/sec | ETA 17:24:53\n",
      "2022-02-27 21:22:07 [INFO]\t[TRAIN] epoch: 1, iter: 590/150000, loss: 1.5795, lr: 0.009965, batch_cost: 0.4206, reader_cost: 0.00014, ips: 28.5334 samples/sec | ETA 17:27:15\n",
      "2022-02-27 21:22:12 [INFO]\t[TRAIN] epoch: 1, iter: 600/150000, loss: 1.5046, lr: 0.009964, batch_cost: 0.4170, reader_cost: 0.00014, ips: 28.7756 samples/sec | ETA 17:18:22\n",
      "2022-02-27 21:22:16 [INFO]\t[TRAIN] epoch: 1, iter: 610/150000, loss: 1.3677, lr: 0.009963, batch_cost: 0.4110, reader_cost: 0.00013, ips: 29.2001 samples/sec | ETA 17:03:12\n",
      "2022-02-27 21:22:20 [INFO]\t[TRAIN] epoch: 1, iter: 620/150000, loss: 1.5178, lr: 0.009963, batch_cost: 0.4271, reader_cost: 0.00014, ips: 28.0986 samples/sec | ETA 17:43:15\n",
      "2022-02-27 21:22:24 [INFO]\t[TRAIN] epoch: 1, iter: 630/150000, loss: 1.4761, lr: 0.009962, batch_cost: 0.4257, reader_cost: 0.00015, ips: 28.1889 samples/sec | ETA 17:39:46\n",
      "2022-02-27 21:22:29 [INFO]\t[TRAIN] epoch: 1, iter: 640/150000, loss: 1.4303, lr: 0.009962, batch_cost: 0.4318, reader_cost: 0.00014, ips: 27.7915 samples/sec | ETA 17:54:51\n",
      "2022-02-27 21:22:33 [INFO]\t[TRAIN] epoch: 1, iter: 650/150000, loss: 1.3119, lr: 0.009961, batch_cost: 0.4226, reader_cost: 0.00017, ips: 28.3926 samples/sec | ETA 17:32:02\n",
      "2022-02-27 21:22:37 [INFO]\t[TRAIN] epoch: 1, iter: 660/150000, loss: 1.5881, lr: 0.009960, batch_cost: 0.4203, reader_cost: 0.00014, ips: 28.5519 samples/sec | ETA 17:26:05\n",
      "2022-02-27 21:22:41 [INFO]\t[TRAIN] epoch: 1, iter: 670/150000, loss: 1.5455, lr: 0.009960, batch_cost: 0.4259, reader_cost: 0.00015, ips: 28.1763 samples/sec | ETA 17:39:58\n",
      "2022-02-27 21:22:46 [INFO]\t[TRAIN] epoch: 1, iter: 680/150000, loss: 1.3643, lr: 0.009959, batch_cost: 0.4339, reader_cost: 0.00014, ips: 27.6551 samples/sec | ETA 17:59:52\n",
      "2022-02-27 21:22:50 [INFO]\t[TRAIN] epoch: 1, iter: 690/150000, loss: 1.3946, lr: 0.009959, batch_cost: 0.4356, reader_cost: 0.00015, ips: 27.5462 samples/sec | ETA 18:04:04\n",
      "2022-02-27 21:22:54 [INFO]\t[TRAIN] epoch: 1, iter: 700/150000, loss: 1.4897, lr: 0.009958, batch_cost: 0.4276, reader_cost: 0.00016, ips: 28.0641 samples/sec | ETA 17:43:59\n",
      "2022-02-27 21:22:59 [INFO]\t[TRAIN] epoch: 1, iter: 710/150000, loss: 1.4673, lr: 0.009957, batch_cost: 0.4320, reader_cost: 0.00015, ips: 27.7762 samples/sec | ETA 17:54:56\n",
      "2022-02-27 21:23:03 [INFO]\t[TRAIN] epoch: 1, iter: 720/150000, loss: 1.7026, lr: 0.009957, batch_cost: 0.4303, reader_cost: 0.00014, ips: 27.8883 samples/sec | ETA 17:50:33\n",
      "2022-02-27 21:23:07 [INFO]\t[TRAIN] epoch: 1, iter: 730/150000, loss: 1.3842, lr: 0.009956, batch_cost: 0.4212, reader_cost: 0.00014, ips: 28.4916 samples/sec | ETA 17:27:49\n",
      "2022-02-27 21:23:11 [INFO]\t[TRAIN] epoch: 1, iter: 740/150000, loss: 1.4682, lr: 0.009956, batch_cost: 0.4304, reader_cost: 0.00014, ips: 27.8783 samples/sec | ETA 17:50:47\n",
      "2022-02-27 21:23:16 [INFO]\t[TRAIN] epoch: 1, iter: 750/150000, loss: 1.4514, lr: 0.009955, batch_cost: 0.4206, reader_cost: 0.00014, ips: 28.5311 samples/sec | ETA 17:26:13\n",
      "2022-02-27 21:23:20 [INFO]\t[TRAIN] epoch: 1, iter: 760/150000, loss: 1.4649, lr: 0.009954, batch_cost: 0.4132, reader_cost: 0.00013, ips: 29.0385 samples/sec | ETA 17:07:52\n",
      "2022-02-27 21:23:24 [INFO]\t[TRAIN] epoch: 1, iter: 770/150000, loss: 1.4145, lr: 0.009954, batch_cost: 0.4155, reader_cost: 0.00014, ips: 28.8836 samples/sec | ETA 17:13:19\n",
      "2022-02-27 21:23:28 [INFO]\t[TRAIN] epoch: 1, iter: 780/150000, loss: 1.6065, lr: 0.009953, batch_cost: 0.4174, reader_cost: 0.00013, ips: 28.7517 samples/sec | ETA 17:17:59\n",
      "2022-02-27 21:23:32 [INFO]\t[TRAIN] epoch: 1, iter: 790/150000, loss: 1.5372, lr: 0.009953, batch_cost: 0.4178, reader_cost: 0.00013, ips: 28.7209 samples/sec | ETA 17:19:01\n",
      "2022-02-27 21:23:37 [INFO]\t[TRAIN] epoch: 1, iter: 800/150000, loss: 1.4887, lr: 0.009952, batch_cost: 0.4443, reader_cost: 0.00014, ips: 27.0078 samples/sec | ETA 18:24:52\n",
      "2022-02-27 21:23:41 [INFO]\t[TRAIN] epoch: 1, iter: 810/150000, loss: 1.4873, lr: 0.009951, batch_cost: 0.4155, reader_cost: 0.00013, ips: 28.8823 samples/sec | ETA 17:13:05\n",
      "2022-02-27 21:23:45 [INFO]\t[TRAIN] epoch: 1, iter: 820/150000, loss: 1.5917, lr: 0.009951, batch_cost: 0.4211, reader_cost: 0.00013, ips: 28.4982 samples/sec | ETA 17:26:56\n",
      "2022-02-27 21:23:49 [INFO]\t[TRAIN] epoch: 1, iter: 830/150000, loss: 1.4943, lr: 0.009950, batch_cost: 0.4220, reader_cost: 0.00015, ips: 28.4339 samples/sec | ETA 17:29:14\n",
      "2022-02-27 21:23:53 [INFO]\t[TRAIN] epoch: 1, iter: 840/150000, loss: 1.5844, lr: 0.009950, batch_cost: 0.4136, reader_cost: 0.00014, ips: 29.0110 samples/sec | ETA 17:08:18\n",
      "2022-02-27 21:23:58 [INFO]\t[TRAIN] epoch: 1, iter: 850/150000, loss: 1.4950, lr: 0.009949, batch_cost: 0.4199, reader_cost: 0.00014, ips: 28.5786 samples/sec | ETA 17:23:47\n",
      "2022-02-27 21:24:02 [INFO]\t[TRAIN] epoch: 1, iter: 860/150000, loss: 1.4173, lr: 0.009948, batch_cost: 0.4208, reader_cost: 0.00013, ips: 28.5182 samples/sec | ETA 17:25:55\n",
      "2022-02-27 21:24:06 [INFO]\t[TRAIN] epoch: 1, iter: 870/150000, loss: 1.3554, lr: 0.009948, batch_cost: 0.4173, reader_cost: 0.00013, ips: 28.7535 samples/sec | ETA 17:17:17\n",
      "2022-02-27 21:24:10 [INFO]\t[TRAIN] epoch: 1, iter: 880/150000, loss: 1.5151, lr: 0.009947, batch_cost: 0.4151, reader_cost: 0.00013, ips: 28.9073 samples/sec | ETA 17:11:42\n",
      "2022-02-27 21:24:14 [INFO]\t[TRAIN] epoch: 1, iter: 890/150000, loss: 1.6060, lr: 0.009947, batch_cost: 0.4214, reader_cost: 0.00015, ips: 28.4766 samples/sec | ETA 17:27:14\n",
      "2022-02-27 21:24:19 [INFO]\t[TRAIN] epoch: 1, iter: 900/150000, loss: 1.4007, lr: 0.009946, batch_cost: 0.4340, reader_cost: 0.00013, ips: 27.6485 samples/sec | ETA 17:58:32\n",
      "2022-02-27 21:24:23 [INFO]\t[TRAIN] epoch: 1, iter: 910/150000, loss: 1.4458, lr: 0.009945, batch_cost: 0.4217, reader_cost: 0.00013, ips: 28.4583 samples/sec | ETA 17:27:46\n",
      "2022-02-27 21:24:27 [INFO]\t[TRAIN] epoch: 1, iter: 920/150000, loss: 1.4852, lr: 0.009945, batch_cost: 0.4225, reader_cost: 0.00014, ips: 28.4037 samples/sec | ETA 17:29:43\n",
      "2022-02-27 21:24:31 [INFO]\t[TRAIN] epoch: 1, iter: 930/150000, loss: 1.6200, lr: 0.009944, batch_cost: 0.4118, reader_cost: 0.00013, ips: 29.1395 samples/sec | ETA 17:03:08\n",
      "2022-02-27 21:24:35 [INFO]\t[TRAIN] epoch: 1, iter: 940/150000, loss: 1.4537, lr: 0.009944, batch_cost: 0.4124, reader_cost: 0.00012, ips: 29.1009 samples/sec | ETA 17:04:26\n",
      "2022-02-27 21:24:40 [INFO]\t[TRAIN] epoch: 1, iter: 950/150000, loss: 1.4587, lr: 0.009943, batch_cost: 0.4166, reader_cost: 0.00013, ips: 28.8048 samples/sec | ETA 17:14:53\n",
      "2022-02-27 21:24:44 [INFO]\t[TRAIN] epoch: 1, iter: 960/150000, loss: 1.2856, lr: 0.009942, batch_cost: 0.4144, reader_cost: 0.00013, ips: 28.9545 samples/sec | ETA 17:09:28\n",
      "2022-02-27 21:24:48 [INFO]\t[TRAIN] epoch: 1, iter: 970/150000, loss: 1.3712, lr: 0.009942, batch_cost: 0.4375, reader_cost: 0.00013, ips: 27.4300 samples/sec | ETA 18:06:37\n",
      "2022-02-27 21:24:52 [INFO]\t[TRAIN] epoch: 1, iter: 980/150000, loss: 1.3895, lr: 0.009941, batch_cost: 0.4170, reader_cost: 0.00015, ips: 28.7782 samples/sec | ETA 17:15:38\n",
      "2022-02-27 21:24:56 [INFO]\t[TRAIN] epoch: 1, iter: 990/150000, loss: 1.4208, lr: 0.009941, batch_cost: 0.4154, reader_cost: 0.00015, ips: 28.8872 samples/sec | ETA 17:11:40\n",
      "2022-02-27 21:25:00 [INFO]\t[TRAIN] epoch: 1, iter: 1000/150000, loss: 1.2528, lr: 0.009940, batch_cost: 0.4147, reader_cost: 0.00013, ips: 28.9370 samples/sec | ETA 17:09:49\n",
      "2022-02-27 21:25:05 [INFO]\t[TRAIN] epoch: 1, iter: 1010/150000, loss: 1.6730, lr: 0.009939, batch_cost: 0.4133, reader_cost: 0.00014, ips: 29.0351 samples/sec | ETA 17:06:16\n",
      "2022-02-27 21:25:09 [INFO]\t[TRAIN] epoch: 1, iter: 1020/150000, loss: 1.4528, lr: 0.009939, batch_cost: 0.4272, reader_cost: 0.00013, ips: 28.0897 samples/sec | ETA 17:40:44\n",
      "2022-02-27 21:25:13 [INFO]\t[TRAIN] epoch: 1, iter: 1030/150000, loss: 1.3473, lr: 0.009938, batch_cost: 0.4350, reader_cost: 0.00014, ips: 27.5855 samples/sec | ETA 18:00:03\n",
      "2022-02-27 21:25:17 [INFO]\t[TRAIN] epoch: 1, iter: 1040/150000, loss: 1.3590, lr: 0.009938, batch_cost: 0.4172, reader_cost: 0.00013, ips: 28.7654 samples/sec | ETA 17:15:41\n",
      "2022-02-27 21:25:22 [INFO]\t[TRAIN] epoch: 1, iter: 1050/150000, loss: 1.5054, lr: 0.009937, batch_cost: 0.4136, reader_cost: 0.00012, ips: 29.0145 samples/sec | ETA 17:06:43\n",
      "2022-02-27 21:25:26 [INFO]\t[TRAIN] epoch: 1, iter: 1060/150000, loss: 1.4375, lr: 0.009936, batch_cost: 0.4157, reader_cost: 0.00013, ips: 28.8643 samples/sec | ETA 17:12:00\n",
      "2022-02-27 21:25:30 [INFO]\t[TRAIN] epoch: 1, iter: 1070/150000, loss: 1.4322, lr: 0.009936, batch_cost: 0.4209, reader_cost: 0.00014, ips: 28.5078 samples/sec | ETA 17:24:50\n",
      "2022-02-27 21:25:34 [INFO]\t[TRAIN] epoch: 1, iter: 1080/150000, loss: 1.4238, lr: 0.009935, batch_cost: 0.4191, reader_cost: 0.00014, ips: 28.6314 samples/sec | ETA 17:20:15\n",
      "2022-02-27 21:25:38 [INFO]\t[TRAIN] epoch: 1, iter: 1090/150000, loss: 1.2850, lr: 0.009935, batch_cost: 0.4279, reader_cost: 0.00014, ips: 28.0466 samples/sec | ETA 17:41:52\n",
      "2022-02-27 21:25:43 [INFO]\t[TRAIN] epoch: 1, iter: 1100/150000, loss: 1.3497, lr: 0.009934, batch_cost: 0.4226, reader_cost: 0.00014, ips: 28.3949 samples/sec | ETA 17:28:46\n",
      "2022-02-27 21:25:47 [INFO]\t[TRAIN] epoch: 1, iter: 1110/150000, loss: 1.3272, lr: 0.009933, batch_cost: 0.4333, reader_cost: 0.00013, ips: 27.6957 samples/sec | ETA 17:55:11\n",
      "2022-02-27 21:25:51 [INFO]\t[TRAIN] epoch: 1, iter: 1120/150000, loss: 1.3851, lr: 0.009933, batch_cost: 0.4325, reader_cost: 0.00014, ips: 27.7432 samples/sec | ETA 17:53:16\n",
      "2022-02-27 21:25:56 [INFO]\t[TRAIN] epoch: 1, iter: 1130/150000, loss: 1.3071, lr: 0.009932, batch_cost: 0.4320, reader_cost: 0.00014, ips: 27.7783 samples/sec | ETA 17:51:50\n",
      "2022-02-27 21:26:00 [INFO]\t[TRAIN] epoch: 1, iter: 1140/150000, loss: 1.5421, lr: 0.009932, batch_cost: 0.4265, reader_cost: 0.00014, ips: 28.1328 samples/sec | ETA 17:38:16\n",
      "2022-02-27 21:26:04 [INFO]\t[TRAIN] epoch: 1, iter: 1150/150000, loss: 1.3081, lr: 0.009931, batch_cost: 0.4182, reader_cost: 0.00013, ips: 28.6953 samples/sec | ETA 17:17:27\n",
      "2022-02-27 21:26:08 [INFO]\t[TRAIN] epoch: 1, iter: 1160/150000, loss: 1.4659, lr: 0.009930, batch_cost: 0.4266, reader_cost: 0.00014, ips: 28.1299 samples/sec | ETA 17:38:14\n",
      "2022-02-27 21:26:13 [INFO]\t[TRAIN] epoch: 1, iter: 1170/150000, loss: 1.3465, lr: 0.009930, batch_cost: 0.4208, reader_cost: 0.00013, ips: 28.5184 samples/sec | ETA 17:23:44\n",
      "2022-02-27 21:26:17 [INFO]\t[TRAIN] epoch: 1, iter: 1180/150000, loss: 1.4087, lr: 0.009929, batch_cost: 0.4165, reader_cost: 0.00013, ips: 28.8120 samples/sec | ETA 17:13:02\n",
      "2022-02-27 21:26:21 [INFO]\t[TRAIN] epoch: 1, iter: 1190/150000, loss: 1.4433, lr: 0.009929, batch_cost: 0.4143, reader_cost: 0.00014, ips: 28.9639 samples/sec | ETA 17:07:33\n",
      "2022-02-27 21:26:25 [INFO]\t[TRAIN] epoch: 1, iter: 1200/150000, loss: 1.4118, lr: 0.009928, batch_cost: 0.4128, reader_cost: 0.00013, ips: 29.0708 samples/sec | ETA 17:03:42\n",
      "2022-02-27 21:26:29 [INFO]\t[TRAIN] epoch: 1, iter: 1210/150000, loss: 1.3848, lr: 0.009927, batch_cost: 0.4143, reader_cost: 0.00014, ips: 28.9631 samples/sec | ETA 17:07:26\n",
      "2022-02-27 21:26:33 [INFO]\t[TRAIN] epoch: 1, iter: 1220/150000, loss: 1.4271, lr: 0.009927, batch_cost: 0.4148, reader_cost: 0.00014, ips: 28.9304 samples/sec | ETA 17:08:32\n",
      "2022-02-27 21:26:37 [INFO]\t[TRAIN] epoch: 1, iter: 1230/150000, loss: 1.4359, lr: 0.009926, batch_cost: 0.4228, reader_cost: 0.00013, ips: 28.3833 samples/sec | ETA 17:28:17\n",
      "2022-02-27 21:26:42 [INFO]\t[TRAIN] epoch: 1, iter: 1240/150000, loss: 1.5222, lr: 0.009926, batch_cost: 0.4204, reader_cost: 0.00012, ips: 28.5457 samples/sec | ETA 17:22:15\n",
      "2022-02-27 21:26:46 [INFO]\t[TRAIN] epoch: 1, iter: 1250/150000, loss: 1.4210, lr: 0.009925, batch_cost: 0.4187, reader_cost: 0.00013, ips: 28.6600 samples/sec | ETA 17:18:01\n",
      "2022-02-27 21:26:50 [INFO]\t[TRAIN] epoch: 1, iter: 1260/150000, loss: 1.3789, lr: 0.009924, batch_cost: 0.4297, reader_cost: 0.00013, ips: 27.9249 samples/sec | ETA 17:45:17\n",
      "2022-02-27 21:26:54 [INFO]\t[TRAIN] epoch: 1, iter: 1270/150000, loss: 1.4777, lr: 0.009924, batch_cost: 0.4254, reader_cost: 0.00013, ips: 28.2088 samples/sec | ETA 17:34:29\n",
      "2022-02-27 21:26:59 [INFO]\t[TRAIN] epoch: 1, iter: 1280/150000, loss: 1.3474, lr: 0.009923, batch_cost: 0.4241, reader_cost: 0.00015, ips: 28.2955 samples/sec | ETA 17:31:11\n",
      "2022-02-27 21:27:03 [INFO]\t[TRAIN] epoch: 1, iter: 1290/150000, loss: 1.4724, lr: 0.009923, batch_cost: 0.4154, reader_cost: 0.00013, ips: 28.8909 samples/sec | ETA 17:09:27\n",
      "2022-02-27 21:27:07 [INFO]\t[TRAIN] epoch: 1, iter: 1300/150000, loss: 1.2641, lr: 0.009922, batch_cost: 0.4170, reader_cost: 0.00014, ips: 28.7749 samples/sec | ETA 17:13:32\n",
      "2022-02-27 21:27:11 [INFO]\t[TRAIN] epoch: 1, iter: 1310/150000, loss: 1.4955, lr: 0.009921, batch_cost: 0.4180, reader_cost: 0.00014, ips: 28.7084 samples/sec | ETA 17:15:51\n",
      "2022-02-27 21:27:15 [INFO]\t[TRAIN] epoch: 1, iter: 1320/150000, loss: 1.4487, lr: 0.009921, batch_cost: 0.4131, reader_cost: 0.00012, ips: 29.0499 samples/sec | ETA 17:03:37\n",
      "2022-02-27 21:27:20 [INFO]\t[TRAIN] epoch: 1, iter: 1330/150000, loss: 1.4227, lr: 0.009920, batch_cost: 0.4201, reader_cost: 0.00014, ips: 28.5666 samples/sec | ETA 17:20:51\n",
      "2022-02-27 21:27:24 [INFO]\t[TRAIN] epoch: 1, iter: 1340/150000, loss: 1.3474, lr: 0.009920, batch_cost: 0.4184, reader_cost: 0.00013, ips: 28.6814 samples/sec | ETA 17:16:37\n",
      "2022-02-27 21:27:28 [INFO]\t[TRAIN] epoch: 1, iter: 1350/150000, loss: 1.6147, lr: 0.009919, batch_cost: 0.4175, reader_cost: 0.00013, ips: 28.7420 samples/sec | ETA 17:14:22\n",
      "2022-02-27 21:27:32 [INFO]\t[TRAIN] epoch: 1, iter: 1360/150000, loss: 1.4286, lr: 0.009918, batch_cost: 0.4173, reader_cost: 0.00013, ips: 28.7584 samples/sec | ETA 17:13:42\n",
      "2022-02-27 21:27:36 [INFO]\t[TRAIN] epoch: 1, iter: 1370/150000, loss: 1.3743, lr: 0.009918, batch_cost: 0.4188, reader_cost: 0.00014, ips: 28.6545 samples/sec | ETA 17:17:23\n",
      "2022-02-27 21:27:40 [INFO]\t[TRAIN] epoch: 1, iter: 1380/150000, loss: 1.5608, lr: 0.009917, batch_cost: 0.4158, reader_cost: 0.00013, ips: 28.8585 samples/sec | ETA 17:09:59\n",
      "2022-02-27 21:27:45 [INFO]\t[TRAIN] epoch: 1, iter: 1390/150000, loss: 1.4321, lr: 0.009917, batch_cost: 0.4173, reader_cost: 0.00013, ips: 28.7547 samples/sec | ETA 17:13:38\n",
      "2022-02-27 21:27:49 [INFO]\t[TRAIN] epoch: 1, iter: 1400/150000, loss: 1.5581, lr: 0.009916, batch_cost: 0.4161, reader_cost: 0.00014, ips: 28.8365 samples/sec | ETA 17:10:38\n",
      "2022-02-27 21:27:53 [INFO]\t[TRAIN] epoch: 1, iter: 1410/150000, loss: 1.4620, lr: 0.009915, batch_cost: 0.4141, reader_cost: 0.00013, ips: 28.9774 samples/sec | ETA 17:05:33\n",
      "2022-02-27 21:27:57 [INFO]\t[TRAIN] epoch: 1, iter: 1420/150000, loss: 1.5973, lr: 0.009915, batch_cost: 0.4161, reader_cost: 0.00014, ips: 28.8409 samples/sec | ETA 17:10:20\n",
      "2022-02-27 21:28:01 [INFO]\t[TRAIN] epoch: 1, iter: 1430/150000, loss: 1.6120, lr: 0.009914, batch_cost: 0.4186, reader_cost: 0.00016, ips: 28.6681 samples/sec | ETA 17:16:29\n",
      "2022-02-27 21:28:05 [INFO]\t[TRAIN] epoch: 1, iter: 1440/150000, loss: 1.3834, lr: 0.009914, batch_cost: 0.4136, reader_cost: 0.00013, ips: 29.0145 samples/sec | ETA 17:04:02\n",
      "2022-02-27 21:28:10 [INFO]\t[TRAIN] epoch: 1, iter: 1450/150000, loss: 1.3066, lr: 0.009913, batch_cost: 0.4219, reader_cost: 0.00014, ips: 28.4448 samples/sec | ETA 17:24:28\n",
      "2022-02-27 21:28:14 [INFO]\t[TRAIN] epoch: 1, iter: 1460/150000, loss: 1.4464, lr: 0.009912, batch_cost: 0.4290, reader_cost: 0.00015, ips: 27.9713 samples/sec | ETA 17:42:05\n",
      "2022-02-27 21:28:18 [INFO]\t[TRAIN] epoch: 1, iter: 1470/150000, loss: 1.3490, lr: 0.009912, batch_cost: 0.4312, reader_cost: 0.00014, ips: 27.8325 samples/sec | ETA 17:47:18\n",
      "2022-02-27 21:28:22 [INFO]\t[TRAIN] epoch: 1, iter: 1480/150000, loss: 1.2609, lr: 0.009911, batch_cost: 0.4157, reader_cost: 0.00013, ips: 28.8644 samples/sec | ETA 17:09:05\n",
      "2022-02-27 21:28:27 [INFO]\t[TRAIN] epoch: 1, iter: 1490/150000, loss: 1.4776, lr: 0.009911, batch_cost: 0.4224, reader_cost: 0.00013, ips: 28.4121 samples/sec | ETA 17:25:24\n",
      "2022-02-27 21:28:31 [INFO]\t[TRAIN] epoch: 1, iter: 1500/150000, loss: 1.3061, lr: 0.009910, batch_cost: 0.4352, reader_cost: 0.00014, ips: 27.5729 samples/sec | ETA 17:57:08\n",
      "2022-02-27 21:28:35 [INFO]\t[TRAIN] epoch: 1, iter: 1510/150000, loss: 1.3816, lr: 0.009909, batch_cost: 0.4301, reader_cost: 0.00132, ips: 27.9012 samples/sec | ETA 17:44:23\n",
      "2022-02-27 21:28:39 [INFO]\t[TRAIN] epoch: 1, iter: 1520/150000, loss: 1.6714, lr: 0.009909, batch_cost: 0.4178, reader_cost: 0.00015, ips: 28.7229 samples/sec | ETA 17:13:52\n",
      "2022-02-27 21:28:44 [INFO]\t[TRAIN] epoch: 1, iter: 1530/150000, loss: 1.4729, lr: 0.009908, batch_cost: 0.4137, reader_cost: 0.00013, ips: 29.0039 samples/sec | ETA 17:03:47\n",
      "2022-02-27 21:28:48 [INFO]\t[TRAIN] epoch: 1, iter: 1540/150000, loss: 1.3760, lr: 0.009908, batch_cost: 0.4134, reader_cost: 0.00013, ips: 29.0250 samples/sec | ETA 17:02:58\n",
      "2022-02-27 21:28:52 [INFO]\t[TRAIN] epoch: 1, iter: 1550/150000, loss: 1.3259, lr: 0.009907, batch_cost: 0.4152, reader_cost: 0.00013, ips: 28.8986 samples/sec | ETA 17:07:23\n",
      "2022-02-27 21:28:56 [INFO]\t[TRAIN] epoch: 1, iter: 1560/150000, loss: 1.3303, lr: 0.009906, batch_cost: 0.4148, reader_cost: 0.00013, ips: 28.9314 samples/sec | ETA 17:06:09\n",
      "2022-02-27 21:29:00 [INFO]\t[TRAIN] epoch: 1, iter: 1570/150000, loss: 1.4459, lr: 0.009906, batch_cost: 0.4172, reader_cost: 0.00014, ips: 28.7635 samples/sec | ETA 17:12:04\n",
      "2022-02-27 21:29:04 [INFO]\t[TRAIN] epoch: 1, iter: 1580/150000, loss: 1.4227, lr: 0.009905, batch_cost: 0.4229, reader_cost: 0.00014, ips: 28.3730 samples/sec | ETA 17:26:12\n",
      "2022-02-27 21:29:09 [INFO]\t[TRAIN] epoch: 1, iter: 1590/150000, loss: 1.4136, lr: 0.009905, batch_cost: 0.4205, reader_cost: 0.00014, ips: 28.5397 samples/sec | ETA 17:20:01\n",
      "2022-02-27 21:29:13 [INFO]\t[TRAIN] epoch: 1, iter: 1600/150000, loss: 1.4537, lr: 0.009904, batch_cost: 0.4147, reader_cost: 0.00014, ips: 28.9377 samples/sec | ETA 17:05:39\n",
      "2022-02-27 21:29:17 [INFO]\t[TRAIN] epoch: 1, iter: 1610/150000, loss: 1.3966, lr: 0.009903, batch_cost: 0.4101, reader_cost: 0.00013, ips: 29.2623 samples/sec | ETA 16:54:12\n",
      "2022-02-27 21:29:21 [INFO]\t[TRAIN] epoch: 1, iter: 1620/150000, loss: 1.3918, lr: 0.009903, batch_cost: 0.4180, reader_cost: 0.00014, ips: 28.7098 samples/sec | ETA 17:13:39\n",
      "2022-02-27 21:29:25 [INFO]\t[TRAIN] epoch: 1, iter: 1630/150000, loss: 1.3928, lr: 0.009902, batch_cost: 0.4188, reader_cost: 0.00014, ips: 28.6523 samples/sec | ETA 17:15:39\n",
      "2022-02-27 21:29:29 [INFO]\t[TRAIN] epoch: 1, iter: 1640/150000, loss: 1.4458, lr: 0.009902, batch_cost: 0.4161, reader_cost: 0.00013, ips: 28.8401 samples/sec | ETA 17:08:50\n",
      "2022-02-27 21:29:34 [INFO]\t[TRAIN] epoch: 1, iter: 1650/150000, loss: 1.6828, lr: 0.009901, batch_cost: 0.4141, reader_cost: 0.00013, ips: 28.9786 samples/sec | ETA 17:03:51\n",
      "2022-02-27 21:29:38 [INFO]\t[TRAIN] epoch: 1, iter: 1660/150000, loss: 1.4683, lr: 0.009900, batch_cost: 0.4214, reader_cost: 0.00015, ips: 28.4736 samples/sec | ETA 17:21:56\n",
      "2022-02-27 21:29:42 [INFO]\t[TRAIN] epoch: 1, iter: 1670/150000, loss: 1.5174, lr: 0.009900, batch_cost: 0.4343, reader_cost: 0.00013, ips: 27.6321 samples/sec | ETA 17:53:36\n",
      "2022-02-27 21:29:46 [INFO]\t[TRAIN] epoch: 1, iter: 1680/150000, loss: 1.2738, lr: 0.009899, batch_cost: 0.4291, reader_cost: 0.00013, ips: 27.9626 samples/sec | ETA 17:40:50\n",
      "2022-02-27 21:29:51 [INFO]\t[TRAIN] epoch: 1, iter: 1690/150000, loss: 1.3828, lr: 0.009899, batch_cost: 0.4302, reader_cost: 0.00014, ips: 27.8923 samples/sec | ETA 17:43:26\n",
      "2022-02-27 21:29:55 [INFO]\t[TRAIN] epoch: 1, iter: 1700/150000, loss: 1.3163, lr: 0.009898, batch_cost: 0.4273, reader_cost: 0.00013, ips: 28.0830 samples/sec | ETA 17:36:09\n",
      "2022-02-27 21:29:59 [INFO]\t[TRAIN] epoch: 1, iter: 1710/150000, loss: 1.3867, lr: 0.009897, batch_cost: 0.4233, reader_cost: 0.00014, ips: 28.3476 samples/sec | ETA 17:26:13\n",
      "2022-02-27 21:30:03 [INFO]\t[TRAIN] epoch: 1, iter: 1720/150000, loss: 1.4524, lr: 0.009897, batch_cost: 0.4244, reader_cost: 0.00015, ips: 28.2762 samples/sec | ETA 17:28:47\n",
      "2022-02-27 21:30:08 [INFO]\t[TRAIN] epoch: 1, iter: 1730/150000, loss: 1.3615, lr: 0.009896, batch_cost: 0.4251, reader_cost: 0.00015, ips: 28.2295 samples/sec | ETA 17:30:27\n",
      "2022-02-27 21:30:12 [INFO]\t[TRAIN] epoch: 1, iter: 1740/150000, loss: 1.4443, lr: 0.009896, batch_cost: 0.4152, reader_cost: 0.00012, ips: 28.9007 samples/sec | ETA 17:05:59\n",
      "2022-02-27 21:30:16 [INFO]\t[TRAIN] epoch: 1, iter: 1750/150000, loss: 1.3771, lr: 0.009895, batch_cost: 0.4175, reader_cost: 0.00013, ips: 28.7395 samples/sec | ETA 17:11:40\n",
      "2022-02-27 21:30:20 [INFO]\t[TRAIN] epoch: 1, iter: 1760/150000, loss: 1.1435, lr: 0.009894, batch_cost: 0.4168, reader_cost: 0.00013, ips: 28.7916 samples/sec | ETA 17:09:44\n",
      "2022-02-27 21:30:24 [INFO]\t[TRAIN] epoch: 1, iter: 1770/150000, loss: 1.4369, lr: 0.009894, batch_cost: 0.4157, reader_cost: 0.00013, ips: 28.8698 samples/sec | ETA 17:06:53\n",
      "2022-02-27 21:30:28 [INFO]\t[TRAIN] epoch: 1, iter: 1780/150000, loss: 1.2576, lr: 0.009893, batch_cost: 0.4186, reader_cost: 0.00013, ips: 28.6675 samples/sec | ETA 17:14:03\n",
      "2022-02-27 21:30:33 [INFO]\t[TRAIN] epoch: 1, iter: 1790/150000, loss: 1.4175, lr: 0.009893, batch_cost: 0.4188, reader_cost: 0.00013, ips: 28.6537 samples/sec | ETA 17:14:29\n",
      "2022-02-27 21:30:37 [INFO]\t[TRAIN] epoch: 1, iter: 1800/150000, loss: 1.4750, lr: 0.009892, batch_cost: 0.4229, reader_cost: 0.00013, ips: 28.3775 samples/sec | ETA 17:24:29\n",
      "2022-02-27 21:30:41 [INFO]\t[TRAIN] epoch: 1, iter: 1810/150000, loss: 1.4078, lr: 0.009891, batch_cost: 0.4209, reader_cost: 0.00014, ips: 28.5098 samples/sec | ETA 17:19:34\n",
      "2022-02-27 21:30:45 [INFO]\t[TRAIN] epoch: 1, iter: 1820/150000, loss: 1.5025, lr: 0.009891, batch_cost: 0.4152, reader_cost: 0.00013, ips: 28.9036 samples/sec | ETA 17:05:20\n",
      "2022-02-27 21:30:49 [INFO]\t[TRAIN] epoch: 1, iter: 1830/150000, loss: 1.3828, lr: 0.009890, batch_cost: 0.4178, reader_cost: 0.00014, ips: 28.7241 samples/sec | ETA 17:11:40\n",
      "2022-02-27 21:30:54 [INFO]\t[TRAIN] epoch: 1, iter: 1840/150000, loss: 1.3358, lr: 0.009890, batch_cost: 0.4148, reader_cost: 0.00013, ips: 28.9303 samples/sec | ETA 17:04:15\n",
      "2022-02-27 21:30:58 [INFO]\t[TRAIN] epoch: 1, iter: 1850/150000, loss: 1.4022, lr: 0.009889, batch_cost: 0.4208, reader_cost: 0.00013, ips: 28.5199 samples/sec | ETA 17:18:55\n",
      "2022-02-27 21:31:02 [INFO]\t[TRAIN] epoch: 1, iter: 1860/150000, loss: 1.3808, lr: 0.009888, batch_cost: 0.4228, reader_cost: 0.00014, ips: 28.3821 samples/sec | ETA 17:23:53\n",
      "2022-02-27 21:31:06 [INFO]\t[TRAIN] epoch: 1, iter: 1870/150000, loss: 1.3230, lr: 0.009888, batch_cost: 0.4166, reader_cost: 0.00015, ips: 28.8031 samples/sec | ETA 17:08:34\n",
      "2022-02-27 21:31:10 [INFO]\t[TRAIN] epoch: 1, iter: 1880/150000, loss: 1.3822, lr: 0.009887, batch_cost: 0.4231, reader_cost: 0.00015, ips: 28.3603 samples/sec | ETA 17:24:33\n",
      "2022-02-27 21:31:15 [INFO]\t[TRAIN] epoch: 1, iter: 1890/150000, loss: 1.3149, lr: 0.009887, batch_cost: 0.4158, reader_cost: 0.00013, ips: 28.8607 samples/sec | ETA 17:06:22\n",
      "2022-02-27 21:31:19 [INFO]\t[TRAIN] epoch: 1, iter: 1900/150000, loss: 1.3463, lr: 0.009886, batch_cost: 0.4174, reader_cost: 0.00014, ips: 28.7519 samples/sec | ETA 17:10:11\n",
      "2022-02-27 21:31:23 [INFO]\t[TRAIN] epoch: 1, iter: 1910/150000, loss: 1.2826, lr: 0.009885, batch_cost: 0.4160, reader_cost: 0.00013, ips: 28.8460 samples/sec | ETA 17:06:45\n",
      "2022-02-27 21:31:27 [INFO]\t[TRAIN] epoch: 1, iter: 1920/150000, loss: 1.3689, lr: 0.009885, batch_cost: 0.4190, reader_cost: 0.00013, ips: 28.6378 samples/sec | ETA 17:14:09\n",
      "2022-02-27 21:31:31 [INFO]\t[TRAIN] epoch: 1, iter: 1930/150000, loss: 1.3958, lr: 0.009884, batch_cost: 0.4173, reader_cost: 0.00013, ips: 28.7564 samples/sec | ETA 17:09:49\n",
      "2022-02-27 21:31:35 [INFO]\t[TRAIN] epoch: 1, iter: 1940/150000, loss: 1.5019, lr: 0.009884, batch_cost: 0.4141, reader_cost: 0.00013, ips: 28.9802 samples/sec | ETA 17:01:48\n",
      "2022-02-27 21:31:40 [INFO]\t[TRAIN] epoch: 1, iter: 1950/150000, loss: 1.4541, lr: 0.009883, batch_cost: 0.4191, reader_cost: 0.00013, ips: 28.6338 samples/sec | ETA 17:14:05\n",
      "2022-02-27 21:31:44 [INFO]\t[TRAIN] epoch: 1, iter: 1960/150000, loss: 1.4084, lr: 0.009882, batch_cost: 0.4165, reader_cost: 0.00015, ips: 28.8114 samples/sec | ETA 17:07:38\n",
      "2022-02-27 21:31:48 [INFO]\t[TRAIN] epoch: 1, iter: 1970/150000, loss: 1.2968, lr: 0.009882, batch_cost: 0.4195, reader_cost: 0.00013, ips: 28.6051 samples/sec | ETA 17:14:59\n",
      "2022-02-27 21:31:52 [INFO]\t[TRAIN] epoch: 1, iter: 1980/150000, loss: 1.4497, lr: 0.009881, batch_cost: 0.4145, reader_cost: 0.00014, ips: 28.9492 samples/sec | ETA 17:02:37\n",
      "2022-02-27 21:31:56 [INFO]\t[TRAIN] epoch: 1, iter: 1990/150000, loss: 1.5774, lr: 0.009881, batch_cost: 0.4104, reader_cost: 0.00013, ips: 29.2391 samples/sec | ETA 16:52:24\n",
      "2022-02-27 21:32:00 [INFO]\t[TRAIN] epoch: 1, iter: 2000/150000, loss: 1.3049, lr: 0.009880, batch_cost: 0.4172, reader_cost: 0.00014, ips: 28.7626 samples/sec | ETA 17:09:06\n",
      "2022-02-27 21:32:05 [INFO]\t[TRAIN] epoch: 1, iter: 2010/150000, loss: 1.5524, lr: 0.009879, batch_cost: 0.4142, reader_cost: 0.00014, ips: 28.9719 samples/sec | ETA 17:01:36\n",
      "2022-02-27 21:32:09 [INFO]\t[TRAIN] epoch: 1, iter: 2020/150000, loss: 1.5247, lr: 0.009879, batch_cost: 0.4115, reader_cost: 0.00013, ips: 29.1585 samples/sec | ETA 16:55:00\n",
      "2022-02-27 21:32:13 [INFO]\t[TRAIN] epoch: 1, iter: 2030/150000, loss: 1.3727, lr: 0.009878, batch_cost: 0.4157, reader_cost: 0.00014, ips: 28.8648 samples/sec | ETA 17:05:15\n",
      "2022-02-27 21:32:17 [INFO]\t[TRAIN] epoch: 1, iter: 2040/150000, loss: 1.3552, lr: 0.009878, batch_cost: 0.4165, reader_cost: 0.00013, ips: 28.8148 samples/sec | ETA 17:06:58\n",
      "2022-02-27 21:32:21 [INFO]\t[TRAIN] epoch: 1, iter: 2050/150000, loss: 1.3695, lr: 0.009877, batch_cost: 0.4212, reader_cost: 0.00014, ips: 28.4881 samples/sec | ETA 17:18:40\n",
      "2022-02-27 21:32:25 [INFO]\t[TRAIN] epoch: 1, iter: 2060/150000, loss: 1.2699, lr: 0.009876, batch_cost: 0.4144, reader_cost: 0.00013, ips: 28.9546 samples/sec | ETA 17:01:52\n",
      "2022-02-27 21:32:29 [INFO]\t[TRAIN] epoch: 1, iter: 2070/150000, loss: 1.3637, lr: 0.009876, batch_cost: 0.4133, reader_cost: 0.00013, ips: 29.0341 samples/sec | ETA 16:59:00\n",
      "2022-02-27 21:32:34 [INFO]\t[TRAIN] epoch: 1, iter: 2080/150000, loss: 1.3956, lr: 0.009875, batch_cost: 0.4196, reader_cost: 0.00013, ips: 28.5999 samples/sec | ETA 17:14:24\n",
      "2022-02-27 21:32:38 [INFO]\t[TRAIN] epoch: 1, iter: 2090/150000, loss: 1.6360, lr: 0.009875, batch_cost: 0.4200, reader_cost: 0.00014, ips: 28.5738 samples/sec | ETA 17:15:16\n",
      "2022-02-27 21:32:42 [INFO]\t[TRAIN] epoch: 1, iter: 2100/150000, loss: 1.5582, lr: 0.009874, batch_cost: 0.4284, reader_cost: 0.00013, ips: 28.0113 samples/sec | ETA 17:36:00\n",
      "2022-02-27 21:32:46 [INFO]\t[TRAIN] epoch: 1, iter: 2110/150000, loss: 1.3638, lr: 0.009873, batch_cost: 0.4158, reader_cost: 0.00013, ips: 28.8602 samples/sec | ETA 17:04:52\n",
      "2022-02-27 21:32:50 [INFO]\t[TRAIN] epoch: 1, iter: 2120/150000, loss: 1.2719, lr: 0.009873, batch_cost: 0.4098, reader_cost: 0.00013, ips: 29.2853 samples/sec | ETA 16:49:55\n",
      "2022-02-27 21:32:55 [INFO]\t[TRAIN] epoch: 1, iter: 2130/150000, loss: 1.3199, lr: 0.009872, batch_cost: 0.4125, reader_cost: 0.00013, ips: 29.0903 samples/sec | ETA 16:56:37\n",
      "2022-02-27 21:32:59 [INFO]\t[TRAIN] epoch: 1, iter: 2140/150000, loss: 1.5969, lr: 0.009872, batch_cost: 0.4155, reader_cost: 0.00014, ips: 28.8800 samples/sec | ETA 17:03:57\n",
      "2022-02-27 21:33:03 [INFO]\t[TRAIN] epoch: 1, iter: 2150/150000, loss: 1.3329, lr: 0.009871, batch_cost: 0.4107, reader_cost: 0.00013, ips: 29.2207 samples/sec | ETA 16:51:57\n",
      "2022-02-27 21:33:07 [INFO]\t[TRAIN] epoch: 1, iter: 2160/150000, loss: 1.1947, lr: 0.009870, batch_cost: 0.4133, reader_cost: 0.00013, ips: 29.0348 samples/sec | ETA 16:58:21\n",
      "2022-02-27 21:33:11 [INFO]\t[TRAIN] epoch: 1, iter: 2170/150000, loss: 1.5887, lr: 0.009870, batch_cost: 0.4210, reader_cost: 0.00014, ips: 28.5006 samples/sec | ETA 17:17:22\n",
      "2022-02-27 21:33:15 [INFO]\t[TRAIN] epoch: 1, iter: 2180/150000, loss: 1.4297, lr: 0.009869, batch_cost: 0.4210, reader_cost: 0.00014, ips: 28.5013 samples/sec | ETA 17:17:17\n",
      "2022-02-27 21:33:20 [INFO]\t[TRAIN] epoch: 1, iter: 2190/150000, loss: 1.4735, lr: 0.009869, batch_cost: 0.4132, reader_cost: 0.00012, ips: 29.0427 samples/sec | ETA 16:57:52\n",
      "2022-02-27 21:33:24 [INFO]\t[TRAIN] epoch: 1, iter: 2200/150000, loss: 1.2158, lr: 0.009868, batch_cost: 0.4150, reader_cost: 0.00013, ips: 28.9140 samples/sec | ETA 17:02:20\n",
      "2022-02-27 21:33:28 [INFO]\t[TRAIN] epoch: 1, iter: 2210/150000, loss: 1.2375, lr: 0.009867, batch_cost: 0.4113, reader_cost: 0.00013, ips: 29.1761 samples/sec | ETA 16:53:05\n",
      "2022-02-27 21:33:32 [INFO]\t[TRAIN] epoch: 1, iter: 2220/150000, loss: 1.4717, lr: 0.009867, batch_cost: 0.4177, reader_cost: 0.00013, ips: 28.7270 samples/sec | ETA 17:08:51\n",
      "2022-02-27 21:33:36 [INFO]\t[TRAIN] epoch: 1, iter: 2230/150000, loss: 1.3390, lr: 0.009866, batch_cost: 0.4142, reader_cost: 0.00013, ips: 28.9682 samples/sec | ETA 17:00:13\n",
      "2022-02-27 21:33:40 [INFO]\t[TRAIN] epoch: 1, iter: 2240/150000, loss: 1.3856, lr: 0.009866, batch_cost: 0.4155, reader_cost: 0.00013, ips: 28.8821 samples/sec | ETA 17:03:11\n",
      "2022-02-27 21:33:44 [INFO]\t[TRAIN] epoch: 1, iter: 2250/150000, loss: 1.2093, lr: 0.009865, batch_cost: 0.4168, reader_cost: 0.00013, ips: 28.7925 samples/sec | ETA 17:06:18\n",
      "2022-02-27 21:33:49 [INFO]\t[TRAIN] epoch: 1, iter: 2260/150000, loss: 1.3112, lr: 0.009864, batch_cost: 0.4202, reader_cost: 0.00014, ips: 28.5574 samples/sec | ETA 17:14:41\n",
      "2022-02-27 21:33:53 [INFO]\t[TRAIN] epoch: 1, iter: 2270/150000, loss: 1.5289, lr: 0.009864, batch_cost: 0.4145, reader_cost: 0.00013, ips: 28.9534 samples/sec | ETA 17:00:27\n",
      "2022-02-27 21:33:57 [INFO]\t[TRAIN] epoch: 1, iter: 2280/150000, loss: 1.3164, lr: 0.009863, batch_cost: 0.4197, reader_cost: 0.00013, ips: 28.5943 samples/sec | ETA 17:13:12\n",
      "2022-02-27 21:34:01 [INFO]\t[TRAIN] epoch: 1, iter: 2290/150000, loss: 1.4365, lr: 0.009863, batch_cost: 0.4158, reader_cost: 0.00013, ips: 28.8614 samples/sec | ETA 17:03:34\n",
      "2022-02-27 21:34:05 [INFO]\t[TRAIN] epoch: 1, iter: 2300/150000, loss: 1.3882, lr: 0.009862, batch_cost: 0.4119, reader_cost: 0.00013, ips: 29.1367 samples/sec | ETA 16:53:50\n",
      "2022-02-27 21:34:09 [INFO]\t[TRAIN] epoch: 1, iter: 2310/150000, loss: 1.5209, lr: 0.009861, batch_cost: 0.4161, reader_cost: 0.00013, ips: 28.8393 samples/sec | ETA 17:04:13\n",
      "2022-02-27 21:34:14 [INFO]\t[TRAIN] epoch: 1, iter: 2320/150000, loss: 1.3682, lr: 0.009861, batch_cost: 0.4317, reader_cost: 0.00015, ips: 27.7944 samples/sec | ETA 17:42:39\n",
      "2022-02-27 21:34:18 [INFO]\t[TRAIN] epoch: 1, iter: 2330/150000, loss: 1.2334, lr: 0.009860, batch_cost: 0.4312, reader_cost: 0.00014, ips: 27.8312 samples/sec | ETA 17:41:10\n",
      "2022-02-27 21:34:22 [INFO]\t[TRAIN] epoch: 1, iter: 2340/150000, loss: 1.2915, lr: 0.009860, batch_cost: 0.4277, reader_cost: 0.00014, ips: 28.0601 samples/sec | ETA 17:32:27\n",
      "2022-02-27 21:34:27 [INFO]\t[TRAIN] epoch: 1, iter: 2350/150000, loss: 1.4602, lr: 0.009859, batch_cost: 0.4241, reader_cost: 0.00013, ips: 28.2922 samples/sec | ETA 17:23:45\n",
      "2022-02-27 21:34:31 [INFO]\t[TRAIN] epoch: 1, iter: 2360/150000, loss: 1.2801, lr: 0.009858, batch_cost: 0.4190, reader_cost: 0.00013, ips: 28.6429 samples/sec | ETA 17:10:54\n",
      "2022-02-27 21:34:35 [INFO]\t[TRAIN] epoch: 1, iter: 2370/150000, loss: 1.5334, lr: 0.009858, batch_cost: 0.4229, reader_cost: 0.00013, ips: 28.3758 samples/sec | ETA 17:20:32\n",
      "2022-02-27 21:34:39 [INFO]\t[TRAIN] epoch: 1, iter: 2380/150000, loss: 1.2613, lr: 0.009857, batch_cost: 0.4246, reader_cost: 0.00013, ips: 28.2637 samples/sec | ETA 17:24:35\n",
      "2022-02-27 21:34:43 [INFO]\t[TRAIN] epoch: 1, iter: 2390/150000, loss: 1.3428, lr: 0.009857, batch_cost: 0.4226, reader_cost: 0.00015, ips: 28.3958 samples/sec | ETA 17:19:39\n",
      "2022-02-27 21:34:48 [INFO]\t[TRAIN] epoch: 1, iter: 2400/150000, loss: 1.3147, lr: 0.009856, batch_cost: 0.4134, reader_cost: 0.00014, ips: 29.0262 samples/sec | ETA 16:57:00\n",
      "2022-02-27 21:34:52 [INFO]\t[TRAIN] epoch: 1, iter: 2410/150000, loss: 1.1940, lr: 0.009855, batch_cost: 0.4154, reader_cost: 0.00014, ips: 28.8894 samples/sec | ETA 17:01:45\n",
      "2022-02-27 21:34:56 [INFO]\t[TRAIN] epoch: 1, iter: 2420/150000, loss: 1.4938, lr: 0.009855, batch_cost: 0.4148, reader_cost: 0.00013, ips: 28.9302 samples/sec | ETA 17:00:14\n",
      "2022-02-27 21:35:00 [INFO]\t[TRAIN] epoch: 1, iter: 2430/150000, loss: 1.5521, lr: 0.009854, batch_cost: 0.4154, reader_cost: 0.00013, ips: 28.8899 samples/sec | ETA 17:01:36\n",
      "2022-02-27 21:35:04 [INFO]\t[TRAIN] epoch: 1, iter: 2440/150000, loss: 1.3993, lr: 0.009854, batch_cost: 0.4188, reader_cost: 0.00013, ips: 28.6532 samples/sec | ETA 17:09:58\n",
      "2022-02-27 21:35:08 [INFO]\t[TRAIN] epoch: 1, iter: 2450/150000, loss: 1.0651, lr: 0.009853, batch_cost: 0.4127, reader_cost: 0.00013, ips: 29.0740 samples/sec | ETA 16:54:59\n",
      "2022-02-27 21:35:13 [INFO]\t[TRAIN] epoch: 1, iter: 2460/150000, loss: 1.4264, lr: 0.009852, batch_cost: 0.4165, reader_cost: 0.00013, ips: 28.8102 samples/sec | ETA 17:04:13\n",
      "2022-02-27 21:35:17 [INFO]\t[TRAIN] epoch: 1, iter: 2470/150000, loss: 1.2916, lr: 0.009852, batch_cost: 0.4178, reader_cost: 0.00014, ips: 28.7188 samples/sec | ETA 17:07:24\n",
      "2022-02-27 21:35:21 [INFO]\t[TRAIN] epoch: 1, iter: 2480/150000, loss: 1.4196, lr: 0.009851, batch_cost: 0.4153, reader_cost: 0.00013, ips: 28.8923 samples/sec | ETA 17:01:10\n",
      "2022-02-27 21:35:25 [INFO]\t[TRAIN] epoch: 1, iter: 2490/150000, loss: 1.2116, lr: 0.009851, batch_cost: 0.4129, reader_cost: 0.00013, ips: 29.0624 samples/sec | ETA 16:55:07\n",
      "2022-02-27 21:35:29 [INFO]\t[TRAIN] epoch: 1, iter: 2500/150000, loss: 1.3191, lr: 0.009850, batch_cost: 0.4186, reader_cost: 0.00014, ips: 28.6651 samples/sec | ETA 17:09:07\n",
      "2022-02-27 21:35:33 [INFO]\t[TRAIN] epoch: 1, iter: 2510/150000, loss: 1.3674, lr: 0.009849, batch_cost: 0.4210, reader_cost: 0.00013, ips: 28.5015 samples/sec | ETA 17:14:57\n",
      "2022-02-27 21:35:38 [INFO]\t[TRAIN] epoch: 1, iter: 2520/150000, loss: 1.5591, lr: 0.009849, batch_cost: 0.4255, reader_cost: 0.00013, ips: 28.1996 samples/sec | ETA 17:25:58\n",
      "2022-02-27 21:35:42 [INFO]\t[TRAIN] epoch: 1, iter: 2530/150000, loss: 1.3866, lr: 0.009848, batch_cost: 0.4251, reader_cost: 0.00014, ips: 28.2302 samples/sec | ETA 17:24:46\n",
      "2022-02-27 21:35:46 [INFO]\t[TRAIN] epoch: 1, iter: 2540/150000, loss: 1.3837, lr: 0.009848, batch_cost: 0.4285, reader_cost: 0.00014, ips: 28.0059 samples/sec | ETA 17:33:03\n",
      "2022-02-27 21:35:50 [INFO]\t[TRAIN] epoch: 1, iter: 2550/150000, loss: 1.3416, lr: 0.009847, batch_cost: 0.4219, reader_cost: 0.00014, ips: 28.4443 samples/sec | ETA 17:16:45\n",
      "2022-02-27 21:35:55 [INFO]\t[TRAIN] epoch: 1, iter: 2560/150000, loss: 1.4396, lr: 0.009846, batch_cost: 0.4226, reader_cost: 0.00014, ips: 28.3969 samples/sec | ETA 17:18:25\n",
      "2022-02-27 21:35:59 [INFO]\t[TRAIN] epoch: 1, iter: 2570/150000, loss: 1.3054, lr: 0.009846, batch_cost: 0.4486, reader_cost: 0.00015, ips: 26.7485 samples/sec | ETA 18:22:20\n",
      "2022-02-27 21:36:03 [INFO]\t[TRAIN] epoch: 1, iter: 2580/150000, loss: 1.2700, lr: 0.009845, batch_cost: 0.4223, reader_cost: 0.00013, ips: 28.4163 samples/sec | ETA 17:17:34\n",
      "2022-02-27 21:36:07 [INFO]\t[TRAIN] epoch: 1, iter: 2590/150000, loss: 1.3261, lr: 0.009845, batch_cost: 0.4154, reader_cost: 0.00014, ips: 28.8897 samples/sec | ETA 17:00:30\n",
      "2022-02-27 21:36:12 [INFO]\t[TRAIN] epoch: 1, iter: 2600/150000, loss: 1.2702, lr: 0.009844, batch_cost: 0.4186, reader_cost: 0.00015, ips: 28.6650 samples/sec | ETA 17:08:26\n",
      "2022-02-27 21:36:16 [INFO]\t[TRAIN] epoch: 1, iter: 2610/150000, loss: 1.5041, lr: 0.009843, batch_cost: 0.4153, reader_cost: 0.00014, ips: 28.8940 samples/sec | ETA 17:00:12\n",
      "2022-02-27 21:36:20 [INFO]\t[TRAIN] epoch: 1, iter: 2620/150000, loss: 1.4116, lr: 0.009843, batch_cost: 0.4109, reader_cost: 0.00014, ips: 29.2013 samples/sec | ETA 16:49:24\n",
      "2022-02-27 21:36:24 [INFO]\t[TRAIN] epoch: 1, iter: 2630/150000, loss: 1.3115, lr: 0.009842, batch_cost: 0.4121, reader_cost: 0.00013, ips: 29.1198 samples/sec | ETA 16:52:09\n",
      "2022-02-27 21:36:28 [INFO]\t[TRAIN] epoch: 1, iter: 2640/150000, loss: 1.3825, lr: 0.009842, batch_cost: 0.4224, reader_cost: 0.00013, ips: 28.4116 samples/sec | ETA 17:17:19\n",
      "2022-02-27 21:36:33 [INFO]\t[TRAIN] epoch: 1, iter: 2650/150000, loss: 1.4452, lr: 0.009841, batch_cost: 0.4231, reader_cost: 0.00014, ips: 28.3603 samples/sec | ETA 17:19:07\n",
      "2022-02-27 21:36:37 [INFO]\t[TRAIN] epoch: 1, iter: 2660/150000, loss: 1.2198, lr: 0.009840, batch_cost: 0.4175, reader_cost: 0.00013, ips: 28.7412 samples/sec | ETA 17:05:17\n",
      "2022-02-27 21:36:41 [INFO]\t[TRAIN] epoch: 1, iter: 2670/150000, loss: 1.3125, lr: 0.009840, batch_cost: 0.4115, reader_cost: 0.00013, ips: 29.1609 samples/sec | ETA 16:50:27\n",
      "2022-02-27 21:36:45 [INFO]\t[TRAIN] epoch: 1, iter: 2680/150000, loss: 1.1638, lr: 0.009839, batch_cost: 0.4181, reader_cost: 0.00013, ips: 28.6990 samples/sec | ETA 17:06:39\n",
      "2022-02-27 21:36:49 [INFO]\t[TRAIN] epoch: 1, iter: 2690/150000, loss: 1.1663, lr: 0.009839, batch_cost: 0.4154, reader_cost: 0.00012, ips: 28.8856 samples/sec | ETA 16:59:57\n",
      "2022-02-27 21:36:53 [INFO]\t[TRAIN] epoch: 1, iter: 2700/150000, loss: 1.4295, lr: 0.009838, batch_cost: 0.4190, reader_cost: 0.00014, ips: 28.6373 samples/sec | ETA 17:08:43\n",
      "2022-02-27 21:36:57 [INFO]\t[TRAIN] epoch: 1, iter: 2710/150000, loss: 1.1977, lr: 0.009837, batch_cost: 0.4141, reader_cost: 0.00013, ips: 28.9792 samples/sec | ETA 16:56:31\n",
      "2022-02-27 21:37:02 [INFO]\t[TRAIN] epoch: 1, iter: 2720/150000, loss: 1.2668, lr: 0.009837, batch_cost: 0.4148, reader_cost: 0.00013, ips: 28.9282 samples/sec | ETA 16:58:14\n",
      "2022-02-27 21:37:06 [INFO]\t[TRAIN] epoch: 1, iter: 2730/150000, loss: 1.3926, lr: 0.009836, batch_cost: 0.4183, reader_cost: 0.00013, ips: 28.6883 samples/sec | ETA 17:06:41\n",
      "2022-02-27 21:37:10 [INFO]\t[TRAIN] epoch: 1, iter: 2740/150000, loss: 1.4157, lr: 0.009836, batch_cost: 0.4140, reader_cost: 0.00013, ips: 28.9820 samples/sec | ETA 16:56:12\n",
      "2022-02-27 21:37:14 [INFO]\t[TRAIN] epoch: 1, iter: 2750/150000, loss: 1.3170, lr: 0.009835, batch_cost: 0.4243, reader_cost: 0.00013, ips: 28.2820 samples/sec | ETA 17:21:17\n",
      "2022-02-27 21:37:18 [INFO]\t[TRAIN] epoch: 1, iter: 2760/150000, loss: 1.3026, lr: 0.009834, batch_cost: 0.4179, reader_cost: 0.00013, ips: 28.7176 samples/sec | ETA 17:05:25\n",
      "2022-02-27 21:37:23 [INFO]\t[TRAIN] epoch: 1, iter: 2770/150000, loss: 1.3147, lr: 0.009834, batch_cost: 0.4192, reader_cost: 0.00014, ips: 28.6250 samples/sec | ETA 17:08:40\n",
      "2022-02-27 21:37:27 [INFO]\t[TRAIN] epoch: 1, iter: 2780/150000, loss: 1.2362, lr: 0.009833, batch_cost: 0.4194, reader_cost: 0.00013, ips: 28.6096 samples/sec | ETA 17:09:09\n",
      "2022-02-27 21:37:31 [INFO]\t[TRAIN] epoch: 1, iter: 2790/150000, loss: 1.2026, lr: 0.009833, batch_cost: 0.4178, reader_cost: 0.00015, ips: 28.7219 samples/sec | ETA 17:05:04\n",
      "2022-02-27 21:37:35 [INFO]\t[TRAIN] epoch: 1, iter: 2800/150000, loss: 1.2452, lr: 0.009832, batch_cost: 0.4177, reader_cost: 0.00014, ips: 28.7290 samples/sec | ETA 17:04:44\n",
      "2022-02-27 21:37:39 [INFO]\t[TRAIN] epoch: 1, iter: 2810/150000, loss: 1.3756, lr: 0.009831, batch_cost: 0.4163, reader_cost: 0.00013, ips: 28.8258 samples/sec | ETA 17:01:14\n",
      "2022-02-27 21:37:43 [INFO]\t[TRAIN] epoch: 1, iter: 2820/150000, loss: 1.2931, lr: 0.009831, batch_cost: 0.4177, reader_cost: 0.00014, ips: 28.7264 samples/sec | ETA 17:04:42\n",
      "2022-02-27 21:37:48 [INFO]\t[TRAIN] epoch: 1, iter: 2830/150000, loss: 1.3228, lr: 0.009830, batch_cost: 0.4164, reader_cost: 0.00013, ips: 28.8206 samples/sec | ETA 17:01:16\n",
      "2022-02-27 21:37:52 [INFO]\t[TRAIN] epoch: 1, iter: 2840/150000, loss: 1.3177, lr: 0.009829, batch_cost: 0.4186, reader_cost: 0.00014, ips: 28.6660 samples/sec | ETA 17:06:43\n",
      "2022-02-27 21:37:56 [INFO]\t[TRAIN] epoch: 1, iter: 2850/150000, loss: 1.2996, lr: 0.009829, batch_cost: 0.4243, reader_cost: 0.00014, ips: 28.2812 samples/sec | ETA 17:20:37\n",
      "2022-02-27 21:38:00 [INFO]\t[TRAIN] epoch: 1, iter: 2860/150000, loss: 1.3635, lr: 0.009828, batch_cost: 0.4165, reader_cost: 0.00013, ips: 28.8111 samples/sec | ETA 17:01:24\n",
      "2022-02-27 21:38:04 [INFO]\t[TRAIN] epoch: 1, iter: 2870/150000, loss: 1.4300, lr: 0.009828, batch_cost: 0.4195, reader_cost: 0.00014, ips: 28.6067 samples/sec | ETA 17:08:38\n",
      "2022-02-27 21:38:09 [INFO]\t[TRAIN] epoch: 1, iter: 2880/150000, loss: 1.4967, lr: 0.009827, batch_cost: 0.4124, reader_cost: 0.00013, ips: 29.0988 samples/sec | ETA 16:51:10\n",
      "2022-02-27 21:38:13 [INFO]\t[TRAIN] epoch: 1, iter: 2890/150000, loss: 1.2721, lr: 0.009826, batch_cost: 0.4156, reader_cost: 0.00013, ips: 28.8708 samples/sec | ETA 16:59:05\n",
      "2022-02-27 21:38:17 [INFO]\t[TRAIN] epoch: 1, iter: 2900/150000, loss: 1.2384, lr: 0.009826, batch_cost: 0.4225, reader_cost: 0.00015, ips: 28.4019 samples/sec | ETA 17:15:50\n",
      "2022-02-27 21:38:21 [INFO]\t[TRAIN] epoch: 1, iter: 2910/150000, loss: 1.2594, lr: 0.009825, batch_cost: 0.4147, reader_cost: 0.00013, ips: 28.9388 samples/sec | ETA 16:56:33\n",
      "2022-02-27 21:38:25 [INFO]\t[TRAIN] epoch: 1, iter: 2920/150000, loss: 1.1907, lr: 0.009825, batch_cost: 0.4208, reader_cost: 0.00013, ips: 28.5204 samples/sec | ETA 17:11:24\n",
      "2022-02-27 21:38:29 [INFO]\t[TRAIN] epoch: 1, iter: 2930/150000, loss: 1.3389, lr: 0.009824, batch_cost: 0.4151, reader_cost: 0.00013, ips: 28.9117 samples/sec | ETA 16:57:22\n",
      "2022-02-27 21:38:34 [INFO]\t[TRAIN] epoch: 1, iter: 2940/150000, loss: 1.2606, lr: 0.009823, batch_cost: 0.4156, reader_cost: 0.00013, ips: 28.8710 samples/sec | ETA 16:58:44\n",
      "2022-02-27 21:38:38 [INFO]\t[TRAIN] epoch: 1, iter: 2950/150000, loss: 1.2626, lr: 0.009823, batch_cost: 0.4206, reader_cost: 0.00015, ips: 28.5300 samples/sec | ETA 17:10:50\n",
      "2022-02-27 21:38:42 [INFO]\t[TRAIN] epoch: 1, iter: 2960/150000, loss: 1.4342, lr: 0.009822, batch_cost: 0.4158, reader_cost: 0.00013, ips: 28.8592 samples/sec | ETA 16:59:01\n",
      "2022-02-27 21:38:46 [INFO]\t[TRAIN] epoch: 1, iter: 2970/150000, loss: 1.4814, lr: 0.009822, batch_cost: 0.4205, reader_cost: 0.00014, ips: 28.5351 samples/sec | ETA 17:10:31\n",
      "2022-02-27 21:38:50 [INFO]\t[TRAIN] epoch: 1, iter: 2980/150000, loss: 1.2673, lr: 0.009821, batch_cost: 0.4149, reader_cost: 0.00013, ips: 28.9233 samples/sec | ETA 16:56:37\n",
      "2022-02-27 21:38:54 [INFO]\t[TRAIN] epoch: 1, iter: 2990/150000, loss: 1.1225, lr: 0.009820, batch_cost: 0.4106, reader_cost: 0.00013, ips: 29.2279 samples/sec | ETA 16:45:57\n",
      "2022-02-27 21:38:59 [INFO]\t[TRAIN] epoch: 1, iter: 3000/150000, loss: 1.3121, lr: 0.009820, batch_cost: 0.4182, reader_cost: 0.00015, ips: 28.6972 samples/sec | ETA 17:04:29\n",
      "2022-02-27 21:39:03 [INFO]\t[TRAIN] epoch: 1, iter: 3010/150000, loss: 1.3372, lr: 0.009819, batch_cost: 0.4169, reader_cost: 0.00014, ips: 28.7816 samples/sec | ETA 17:01:25\n",
      "2022-02-27 21:39:07 [INFO]\t[TRAIN] epoch: 1, iter: 3020/150000, loss: 1.4353, lr: 0.009819, batch_cost: 0.4189, reader_cost: 0.00015, ips: 28.6478 samples/sec | ETA 17:06:07\n",
      "2022-02-27 21:39:11 [INFO]\t[TRAIN] epoch: 1, iter: 3030/150000, loss: 1.4633, lr: 0.009818, batch_cost: 0.4197, reader_cost: 0.00013, ips: 28.5925 samples/sec | ETA 17:08:01\n",
      "2022-02-27 21:39:15 [INFO]\t[TRAIN] epoch: 1, iter: 3040/150000, loss: 1.2279, lr: 0.009817, batch_cost: 0.4203, reader_cost: 0.00014, ips: 28.5490 samples/sec | ETA 17:09:31\n",
      "2022-02-27 21:39:20 [INFO]\t[TRAIN] epoch: 1, iter: 3050/150000, loss: 1.1711, lr: 0.009817, batch_cost: 0.4194, reader_cost: 0.00014, ips: 28.6094 samples/sec | ETA 17:07:17\n",
      "2022-02-27 21:39:24 [INFO]\t[TRAIN] epoch: 1, iter: 3060/150000, loss: 1.1893, lr: 0.009816, batch_cost: 0.4173, reader_cost: 0.00013, ips: 28.7552 samples/sec | ETA 17:02:00\n",
      "2022-02-27 21:39:28 [INFO]\t[TRAIN] epoch: 1, iter: 3070/150000, loss: 1.2081, lr: 0.009816, batch_cost: 0.4308, reader_cost: 0.00014, ips: 27.8550 samples/sec | ETA 17:34:57\n",
      "2022-02-27 21:39:33 [INFO]\t[TRAIN] epoch: 1, iter: 3080/150000, loss: 1.1924, lr: 0.009815, batch_cost: 0.4565, reader_cost: 0.00013, ips: 26.2892 samples/sec | ETA 18:37:43\n",
      "2022-02-27 21:39:37 [INFO]\t[TRAIN] epoch: 1, iter: 3090/150000, loss: 1.3856, lr: 0.009814, batch_cost: 0.4245, reader_cost: 0.00016, ips: 28.2687 samples/sec | ETA 17:19:22\n",
      "2022-02-27 21:39:41 [INFO]\t[TRAIN] epoch: 1, iter: 3100/150000, loss: 1.4124, lr: 0.009814, batch_cost: 0.4218, reader_cost: 0.00013, ips: 28.4494 samples/sec | ETA 17:12:42\n",
      "2022-02-27 21:39:45 [INFO]\t[TRAIN] epoch: 1, iter: 3110/150000, loss: 1.3576, lr: 0.009813, batch_cost: 0.4194, reader_cost: 0.00013, ips: 28.6107 samples/sec | ETA 17:06:49\n",
      "2022-02-27 21:39:49 [INFO]\t[TRAIN] epoch: 1, iter: 3120/150000, loss: 1.4946, lr: 0.009813, batch_cost: 0.4240, reader_cost: 0.00014, ips: 28.3045 samples/sec | ETA 17:17:51\n",
      "2022-02-27 21:39:54 [INFO]\t[TRAIN] epoch: 1, iter: 3130/150000, loss: 1.2792, lr: 0.009812, batch_cost: 0.4185, reader_cost: 0.00014, ips: 28.6763 samples/sec | ETA 17:04:19\n",
      "2022-02-27 21:39:58 [INFO]\t[TRAIN] epoch: 1, iter: 3140/150000, loss: 1.3438, lr: 0.009811, batch_cost: 0.4178, reader_cost: 0.00014, ips: 28.7227 samples/sec | ETA 17:02:36\n",
      "2022-02-27 21:40:02 [INFO]\t[TRAIN] epoch: 1, iter: 3150/150000, loss: 1.3390, lr: 0.009811, batch_cost: 0.4271, reader_cost: 0.00014, ips: 28.0996 samples/sec | ETA 17:25:12\n",
      "2022-02-27 21:40:06 [INFO]\t[TRAIN] epoch: 1, iter: 3160/150000, loss: 1.1567, lr: 0.009810, batch_cost: 0.4182, reader_cost: 0.00013, ips: 28.6932 samples/sec | ETA 17:03:31\n",
      "2022-02-27 21:40:11 [INFO]\t[TRAIN] epoch: 1, iter: 3170/150000, loss: 1.2860, lr: 0.009810, batch_cost: 0.4285, reader_cost: 0.00013, ips: 28.0044 samples/sec | ETA 17:28:37\n",
      "2022-02-27 21:40:15 [INFO]\t[TRAIN] epoch: 1, iter: 3180/150000, loss: 1.3756, lr: 0.009809, batch_cost: 0.4148, reader_cost: 0.00013, ips: 28.9302 samples/sec | ETA 16:54:59\n",
      "2022-02-27 21:40:19 [INFO]\t[TRAIN] epoch: 1, iter: 3190/150000, loss: 1.2265, lr: 0.009808, batch_cost: 0.4201, reader_cost: 0.00014, ips: 28.5640 samples/sec | ETA 17:07:56\n",
      "2022-02-27 21:40:23 [INFO]\t[TRAIN] epoch: 1, iter: 3200/150000, loss: 1.2898, lr: 0.009808, batch_cost: 0.4170, reader_cost: 0.00013, ips: 28.7745 samples/sec | ETA 17:00:20\n",
      "2022-02-27 21:40:27 [INFO]\t[TRAIN] epoch: 1, iter: 3210/150000, loss: 1.3155, lr: 0.009807, batch_cost: 0.4175, reader_cost: 0.00013, ips: 28.7449 samples/sec | ETA 17:01:19\n",
      "2022-02-27 21:40:31 [INFO]\t[TRAIN] epoch: 1, iter: 3220/150000, loss: 1.3247, lr: 0.009807, batch_cost: 0.4199, reader_cost: 0.00013, ips: 28.5801 samples/sec | ETA 17:07:08\n",
      "2022-02-27 21:40:36 [INFO]\t[TRAIN] epoch: 1, iter: 3230/150000, loss: 1.2909, lr: 0.009806, batch_cost: 0.4172, reader_cost: 0.00013, ips: 28.7624 samples/sec | ETA 17:00:34\n",
      "2022-02-27 21:40:40 [INFO]\t[TRAIN] epoch: 1, iter: 3240/150000, loss: 1.2147, lr: 0.009805, batch_cost: 0.4172, reader_cost: 0.00014, ips: 28.7665 samples/sec | ETA 17:00:21\n",
      "2022-02-27 21:40:44 [INFO]\t[TRAIN] epoch: 1, iter: 3250/150000, loss: 1.3439, lr: 0.009805, batch_cost: 0.4171, reader_cost: 0.00013, ips: 28.7680 samples/sec | ETA 17:00:13\n",
      "2022-02-27 21:40:48 [INFO]\t[TRAIN] epoch: 1, iter: 3260/150000, loss: 1.2261, lr: 0.009804, batch_cost: 0.4267, reader_cost: 0.00014, ips: 28.1224 samples/sec | ETA 17:23:34\n",
      "2022-02-27 21:40:52 [INFO]\t[TRAIN] epoch: 1, iter: 3270/150000, loss: 1.3515, lr: 0.009804, batch_cost: 0.4163, reader_cost: 0.00013, ips: 28.8256 samples/sec | ETA 16:58:03\n",
      "2022-02-27 21:40:57 [INFO]\t[TRAIN] epoch: 1, iter: 3280/150000, loss: 1.4874, lr: 0.009803, batch_cost: 0.4243, reader_cost: 0.00013, ips: 28.2847 samples/sec | ETA 17:17:27\n",
      "2022-02-27 21:41:01 [INFO]\t[TRAIN] epoch: 1, iter: 3290/150000, loss: 1.1366, lr: 0.009802, batch_cost: 0.4191, reader_cost: 0.00013, ips: 28.6334 samples/sec | ETA 17:04:44\n",
      "2022-02-27 21:41:05 [INFO]\t[TRAIN] epoch: 1, iter: 3300/150000, loss: 1.1688, lr: 0.009802, batch_cost: 0.4248, reader_cost: 0.00015, ips: 28.2497 samples/sec | ETA 17:18:35\n",
      "2022-02-27 21:41:09 [INFO]\t[TRAIN] epoch: 1, iter: 3310/150000, loss: 1.3155, lr: 0.009801, batch_cost: 0.4184, reader_cost: 0.00013, ips: 28.6838 samples/sec | ETA 17:02:48\n",
      "2022-02-27 21:41:13 [INFO]\t[TRAIN] epoch: 1, iter: 3320/150000, loss: 1.4429, lr: 0.009801, batch_cost: 0.4118, reader_cost: 0.00013, ips: 29.1389 samples/sec | ETA 16:46:45\n",
      "2022-02-27 21:41:18 [INFO]\t[TRAIN] epoch: 1, iter: 3330/150000, loss: 1.1989, lr: 0.009800, batch_cost: 0.4178, reader_cost: 0.00013, ips: 28.7242 samples/sec | ETA 17:01:13\n",
      "2022-02-27 21:41:22 [INFO]\t[TRAIN] epoch: 1, iter: 3340/150000, loss: 1.1757, lr: 0.009799, batch_cost: 0.4163, reader_cost: 0.00013, ips: 28.8234 samples/sec | ETA 16:57:38\n",
      "2022-02-27 21:41:26 [INFO]\t[TRAIN] epoch: 1, iter: 3350/150000, loss: 1.0442, lr: 0.009799, batch_cost: 0.4132, reader_cost: 0.00013, ips: 29.0439 samples/sec | ETA 16:49:51\n",
      "2022-02-27 21:41:30 [INFO]\t[TRAIN] epoch: 1, iter: 3360/150000, loss: 1.3338, lr: 0.009798, batch_cost: 0.4176, reader_cost: 0.00013, ips: 28.7375 samples/sec | ETA 17:00:32\n",
      "2022-02-27 21:41:34 [INFO]\t[TRAIN] epoch: 1, iter: 3370/150000, loss: 1.2914, lr: 0.009798, batch_cost: 0.4158, reader_cost: 0.00014, ips: 28.8612 samples/sec | ETA 16:56:06\n",
      "2022-02-27 21:41:38 [INFO]\t[TRAIN] epoch: 1, iter: 3380/150000, loss: 1.3127, lr: 0.009797, batch_cost: 0.4149, reader_cost: 0.00014, ips: 28.9223 samples/sec | ETA 16:53:53\n",
      "2022-02-27 21:41:43 [INFO]\t[TRAIN] epoch: 1, iter: 3390/150000, loss: 1.3202, lr: 0.009796, batch_cost: 0.4176, reader_cost: 0.00013, ips: 28.7372 samples/sec | ETA 17:00:21\n",
      "2022-02-27 21:41:47 [INFO]\t[TRAIN] epoch: 1, iter: 3400/150000, loss: 1.2075, lr: 0.009796, batch_cost: 0.4153, reader_cost: 0.00013, ips: 28.8914 samples/sec | ETA 16:54:50\n",
      "2022-02-27 21:41:51 [INFO]\t[TRAIN] epoch: 1, iter: 3410/150000, loss: 1.1931, lr: 0.009795, batch_cost: 0.4173, reader_cost: 0.00013, ips: 28.7562 samples/sec | ETA 16:59:32\n",
      "2022-02-27 21:41:55 [INFO]\t[TRAIN] epoch: 1, iter: 3420/150000, loss: 1.3504, lr: 0.009795, batch_cost: 0.4134, reader_cost: 0.00013, ips: 29.0296 samples/sec | ETA 16:49:51\n",
      "2022-02-27 21:41:59 [INFO]\t[TRAIN] epoch: 1, iter: 3430/150000, loss: 1.5472, lr: 0.009794, batch_cost: 0.4133, reader_cost: 0.00013, ips: 29.0346 samples/sec | ETA 16:49:37\n",
      "2022-02-27 21:42:03 [INFO]\t[TRAIN] epoch: 1, iter: 3440/150000, loss: 1.4305, lr: 0.009793, batch_cost: 0.4152, reader_cost: 0.00014, ips: 28.8988 samples/sec | ETA 16:54:17\n",
      "2022-02-27 21:42:08 [INFO]\t[TRAIN] epoch: 1, iter: 3450/150000, loss: 1.1780, lr: 0.009793, batch_cost: 0.4266, reader_cost: 0.00014, ips: 28.1293 samples/sec | ETA 17:21:58\n",
      "2022-02-27 21:42:12 [INFO]\t[TRAIN] epoch: 1, iter: 3460/150000, loss: 1.2695, lr: 0.009792, batch_cost: 0.4192, reader_cost: 0.00014, ips: 28.6283 samples/sec | ETA 17:03:44\n",
      "2022-02-27 21:42:16 [INFO]\t[TRAIN] epoch: 1, iter: 3470/150000, loss: 1.1101, lr: 0.009792, batch_cost: 0.4202, reader_cost: 0.00013, ips: 28.5611 samples/sec | ETA 17:06:04\n",
      "2022-02-27 21:42:20 [INFO]\t[TRAIN] epoch: 1, iter: 3480/150000, loss: 1.1566, lr: 0.009791, batch_cost: 0.4160, reader_cost: 0.00013, ips: 28.8442 samples/sec | ETA 16:55:56\n",
      "2022-02-27 21:42:24 [INFO]\t[TRAIN] epoch: 1, iter: 3490/150000, loss: 1.3666, lr: 0.009790, batch_cost: 0.4242, reader_cost: 0.00014, ips: 28.2903 samples/sec | ETA 17:15:45\n",
      "2022-02-27 21:42:29 [INFO]\t[TRAIN] epoch: 1, iter: 3500/150000, loss: 1.1560, lr: 0.009790, batch_cost: 0.4258, reader_cost: 0.00014, ips: 28.1811 samples/sec | ETA 17:19:42\n",
      "2022-02-27 21:42:33 [INFO]\t[TRAIN] epoch: 1, iter: 3510/150000, loss: 1.3331, lr: 0.009789, batch_cost: 0.4118, reader_cost: 0.00013, ips: 29.1376 samples/sec | ETA 16:45:30\n",
      "2022-02-27 21:42:37 [INFO]\t[TRAIN] epoch: 1, iter: 3520/150000, loss: 1.1775, lr: 0.009789, batch_cost: 0.4166, reader_cost: 0.00013, ips: 28.8024 samples/sec | ETA 16:57:08\n",
      "2022-02-27 21:42:41 [INFO]\t[TRAIN] epoch: 1, iter: 3530/150000, loss: 1.3446, lr: 0.009788, batch_cost: 0.4150, reader_cost: 0.00013, ips: 28.9162 samples/sec | ETA 16:53:03\n",
      "2022-02-27 21:42:45 [INFO]\t[TRAIN] epoch: 1, iter: 3540/150000, loss: 1.2367, lr: 0.009787, batch_cost: 0.4141, reader_cost: 0.00013, ips: 28.9814 samples/sec | ETA 16:50:43\n",
      "2022-02-27 21:42:49 [INFO]\t[TRAIN] epoch: 1, iter: 3550/150000, loss: 1.2868, lr: 0.009787, batch_cost: 0.4182, reader_cost: 0.00014, ips: 28.6921 samples/sec | ETA 17:00:50\n",
      "2022-02-27 21:42:54 [INFO]\t[TRAIN] epoch: 1, iter: 3560/150000, loss: 1.3656, lr: 0.009786, batch_cost: 0.4231, reader_cost: 0.00014, ips: 28.3632 samples/sec | ETA 17:12:36\n",
      "2022-02-27 21:42:58 [INFO]\t[TRAIN] epoch: 1, iter: 3570/150000, loss: 1.3576, lr: 0.009786, batch_cost: 0.4161, reader_cost: 0.00013, ips: 28.8367 samples/sec | ETA 16:55:34\n",
      "2022-02-27 21:43:02 [INFO]\t[TRAIN] epoch: 1, iter: 3580/150000, loss: 1.2461, lr: 0.009785, batch_cost: 0.4171, reader_cost: 0.00013, ips: 28.7705 samples/sec | ETA 16:57:50\n",
      "2022-02-27 21:43:06 [INFO]\t[TRAIN] epoch: 1, iter: 3590/150000, loss: 1.2755, lr: 0.009784, batch_cost: 0.4213, reader_cost: 0.00014, ips: 28.4799 samples/sec | ETA 17:08:09\n",
      "2022-02-27 21:43:10 [INFO]\t[TRAIN] epoch: 1, iter: 3600/150000, loss: 1.3942, lr: 0.009784, batch_cost: 0.4209, reader_cost: 0.00016, ips: 28.5076 samples/sec | ETA 17:07:05\n",
      "2022-02-27 21:43:15 [INFO]\t[TRAIN] epoch: 1, iter: 3610/150000, loss: 1.2434, lr: 0.009783, batch_cost: 0.4148, reader_cost: 0.00013, ips: 28.9306 samples/sec | ETA 16:52:00\n",
      "2022-02-27 21:43:19 [INFO]\t[TRAIN] epoch: 1, iter: 3620/150000, loss: 1.2000, lr: 0.009783, batch_cost: 0.4093, reader_cost: 0.00013, ips: 29.3186 samples/sec | ETA 16:38:32\n",
      "2022-02-27 21:43:23 [INFO]\t[TRAIN] epoch: 1, iter: 3630/150000, loss: 1.4198, lr: 0.009782, batch_cost: 0.4207, reader_cost: 0.00014, ips: 28.5254 samples/sec | ETA 17:06:14\n",
      "2022-02-27 21:43:27 [INFO]\t[TRAIN] epoch: 1, iter: 3640/150000, loss: 1.1732, lr: 0.009781, batch_cost: 0.4164, reader_cost: 0.00013, ips: 28.8158 samples/sec | ETA 16:55:49\n",
      "2022-02-27 21:43:31 [INFO]\t[TRAIN] epoch: 1, iter: 3650/150000, loss: 1.1947, lr: 0.009781, batch_cost: 0.4154, reader_cost: 0.00014, ips: 28.8898 samples/sec | ETA 16:53:09\n",
      "2022-02-27 21:43:35 [INFO]\t[TRAIN] epoch: 1, iter: 3660/150000, loss: 1.5424, lr: 0.009780, batch_cost: 0.4142, reader_cost: 0.00013, ips: 28.9715 samples/sec | ETA 16:50:14\n",
      "2022-02-27 21:43:39 [INFO]\t[TRAIN] epoch: 1, iter: 3670/150000, loss: 1.1497, lr: 0.009780, batch_cost: 0.4099, reader_cost: 0.00013, ips: 29.2764 samples/sec | ETA 16:39:38\n",
      "2022-02-27 21:43:44 [INFO]\t[TRAIN] epoch: 1, iter: 3680/150000, loss: 1.1410, lr: 0.009779, batch_cost: 0.4167, reader_cost: 0.00013, ips: 28.8008 samples/sec | ETA 16:56:04\n",
      "2022-02-27 21:43:48 [INFO]\t[TRAIN] epoch: 1, iter: 3690/150000, loss: 1.2962, lr: 0.009778, batch_cost: 0.4195, reader_cost: 0.00014, ips: 28.6036 samples/sec | ETA 17:03:01\n",
      "2022-02-27 21:43:52 [INFO]\t[TRAIN] epoch: 1, iter: 3700/150000, loss: 1.2053, lr: 0.009778, batch_cost: 0.4180, reader_cost: 0.00014, ips: 28.7062 samples/sec | ETA 16:59:17\n",
      "2022-02-27 21:43:56 [INFO]\t[TRAIN] epoch: 1, iter: 3710/150000, loss: 1.2870, lr: 0.009777, batch_cost: 0.4220, reader_cost: 0.00014, ips: 28.4369 samples/sec | ETA 17:08:52\n",
      "2022-02-27 21:44:00 [INFO]\t[TRAIN] epoch: 1, iter: 3720/150000, loss: 1.3571, lr: 0.009777, batch_cost: 0.4122, reader_cost: 0.00014, ips: 29.1090 samples/sec | ETA 16:45:03\n",
      "2022-02-27 21:44:04 [INFO]\t[TRAIN] epoch: 1, iter: 3730/150000, loss: 1.6039, lr: 0.009776, batch_cost: 0.4155, reader_cost: 0.00013, ips: 28.8784 samples/sec | ETA 16:53:00\n",
      "2022-02-27 21:44:09 [INFO]\t[TRAIN] epoch: 1, iter: 3740/150000, loss: 1.3925, lr: 0.009775, batch_cost: 0.4183, reader_cost: 0.00013, ips: 28.6869 samples/sec | ETA 16:59:41\n",
      "2022-02-27 21:44:13 [INFO]\t[TRAIN] epoch: 1, iter: 3750/150000, loss: 1.2516, lr: 0.009775, batch_cost: 0.4188, reader_cost: 0.00015, ips: 28.6545 samples/sec | ETA 17:00:46\n",
      "2022-02-27 21:44:17 [INFO]\t[TRAIN] epoch: 1, iter: 3760/150000, loss: 1.3134, lr: 0.009774, batch_cost: 0.4151, reader_cost: 0.00013, ips: 28.9090 samples/sec | ETA 16:51:43\n",
      "2022-02-27 21:44:21 [INFO]\t[TRAIN] epoch: 1, iter: 3770/150000, loss: 1.1631, lr: 0.009774, batch_cost: 0.4166, reader_cost: 0.00014, ips: 28.8057 samples/sec | ETA 16:55:17\n",
      "2022-02-27 21:44:25 [INFO]\t[TRAIN] epoch: 1, iter: 3780/150000, loss: 1.1576, lr: 0.009773, batch_cost: 0.4160, reader_cost: 0.00014, ips: 28.8450 samples/sec | ETA 16:53:50\n",
      "2022-02-27 21:44:29 [INFO]\t[TRAIN] epoch: 1, iter: 3790/150000, loss: 1.4170, lr: 0.009772, batch_cost: 0.4176, reader_cost: 0.00013, ips: 28.7334 samples/sec | ETA 16:57:42\n",
      "2022-02-27 21:44:34 [INFO]\t[TRAIN] epoch: 1, iter: 3800/150000, loss: 1.2485, lr: 0.009772, batch_cost: 0.4136, reader_cost: 0.00012, ips: 29.0169 samples/sec | ETA 16:47:41\n",
      "2022-02-27 21:44:38 [INFO]\t[TRAIN] epoch: 1, iter: 3810/150000, loss: 1.2878, lr: 0.009771, batch_cost: 0.4155, reader_cost: 0.00014, ips: 28.8797 samples/sec | ETA 16:52:24\n",
      "2022-02-27 21:44:42 [INFO]\t[TRAIN] epoch: 1, iter: 3820/150000, loss: 1.3369, lr: 0.009771, batch_cost: 0.4153, reader_cost: 0.00013, ips: 28.8978 samples/sec | ETA 16:51:42\n",
      "2022-02-27 21:44:46 [INFO]\t[TRAIN] epoch: 1, iter: 3830/150000, loss: 1.4405, lr: 0.009770, batch_cost: 0.4116, reader_cost: 0.00012, ips: 29.1515 samples/sec | ETA 16:42:49\n",
      "2022-02-27 21:44:50 [INFO]\t[TRAIN] epoch: 1, iter: 3840/150000, loss: 1.4737, lr: 0.009769, batch_cost: 0.4211, reader_cost: 0.00015, ips: 28.4949 samples/sec | ETA 17:05:52\n",
      "2022-02-27 21:44:54 [INFO]\t[TRAIN] epoch: 1, iter: 3850/150000, loss: 1.1438, lr: 0.009769, batch_cost: 0.4195, reader_cost: 0.00013, ips: 28.6070 samples/sec | ETA 17:01:46\n",
      "2022-02-27 21:44:59 [INFO]\t[TRAIN] epoch: 1, iter: 3860/150000, loss: 1.3499, lr: 0.009768, batch_cost: 0.4248, reader_cost: 0.00013, ips: 28.2503 samples/sec | ETA 17:14:36\n",
      "2022-02-27 21:45:03 [INFO]\t[TRAIN] epoch: 1, iter: 3870/150000, loss: 1.1661, lr: 0.009768, batch_cost: 0.4223, reader_cost: 0.00013, ips: 28.4191 samples/sec | ETA 17:08:23\n",
      "2022-02-27 21:45:07 [INFO]\t[TRAIN] epoch: 1, iter: 3880/150000, loss: 1.1544, lr: 0.009767, batch_cost: 0.4185, reader_cost: 0.00013, ips: 28.6766 samples/sec | ETA 16:59:05\n",
      "2022-02-27 21:45:11 [INFO]\t[TRAIN] epoch: 1, iter: 3890/150000, loss: 1.2335, lr: 0.009766, batch_cost: 0.4313, reader_cost: 0.00013, ips: 27.8239 samples/sec | ETA 17:30:14\n",
      "2022-02-27 21:45:16 [INFO]\t[TRAIN] epoch: 1, iter: 3900/150000, loss: 1.2268, lr: 0.009766, batch_cost: 0.4230, reader_cost: 0.00014, ips: 28.3707 samples/sec | ETA 17:09:56\n",
      "2022-02-27 21:45:20 [INFO]\t[TRAIN] epoch: 1, iter: 3910/150000, loss: 1.2524, lr: 0.009765, batch_cost: 0.4332, reader_cost: 0.00014, ips: 27.7007 samples/sec | ETA 17:34:46\n",
      "2022-02-27 21:45:24 [INFO]\t[TRAIN] epoch: 1, iter: 3920/150000, loss: 1.3004, lr: 0.009765, batch_cost: 0.4165, reader_cost: 0.00012, ips: 28.8136 samples/sec | ETA 16:53:58\n",
      "2022-02-27 21:45:28 [INFO]\t[TRAIN] epoch: 1, iter: 3930/150000, loss: 1.1962, lr: 0.009764, batch_cost: 0.4180, reader_cost: 0.00013, ips: 28.7089 samples/sec | ETA 16:57:35\n",
      "2022-02-27 21:45:33 [INFO]\t[TRAIN] epoch: 1, iter: 3940/150000, loss: 1.2250, lr: 0.009763, batch_cost: 0.4231, reader_cost: 0.00013, ips: 28.3623 samples/sec | ETA 17:09:57\n",
      "2022-02-27 21:45:37 [INFO]\t[TRAIN] epoch: 1, iter: 3950/150000, loss: 1.0847, lr: 0.009763, batch_cost: 0.4190, reader_cost: 0.00013, ips: 28.6398 samples/sec | ETA 16:59:54\n",
      "2022-02-27 21:45:41 [INFO]\t[TRAIN] epoch: 1, iter: 3960/150000, loss: 1.3483, lr: 0.009762, batch_cost: 0.4172, reader_cost: 0.00013, ips: 28.7664 samples/sec | ETA 16:55:21\n",
      "2022-02-27 21:45:45 [INFO]\t[TRAIN] epoch: 1, iter: 3970/150000, loss: 1.3733, lr: 0.009762, batch_cost: 0.4139, reader_cost: 0.00013, ips: 28.9892 samples/sec | ETA 16:47:28\n",
      "2022-02-27 21:45:49 [INFO]\t[TRAIN] epoch: 1, iter: 3980/150000, loss: 1.4189, lr: 0.009761, batch_cost: 0.4201, reader_cost: 0.00013, ips: 28.5643 samples/sec | ETA 17:02:23\n",
      "2022-02-27 21:45:53 [INFO]\t[TRAIN] epoch: 1, iter: 3990/150000, loss: 1.3643, lr: 0.009760, batch_cost: 0.4226, reader_cost: 0.00015, ips: 28.3974 samples/sec | ETA 17:08:20\n",
      "2022-02-27 21:45:58 [INFO]\t[TRAIN] epoch: 1, iter: 4000/150000, loss: 1.2455, lr: 0.009760, batch_cost: 0.4134, reader_cost: 0.00013, ips: 29.0265 samples/sec | ETA 16:45:58\n",
      "2022-02-27 21:46:02 [INFO]\t[TRAIN] epoch: 1, iter: 4010/150000, loss: 1.2579, lr: 0.009759, batch_cost: 0.4194, reader_cost: 0.00014, ips: 28.6147 samples/sec | ETA 17:00:23\n",
      "2022-02-27 21:46:06 [INFO]\t[TRAIN] epoch: 1, iter: 4020/150000, loss: 1.3029, lr: 0.009759, batch_cost: 0.4110, reader_cost: 0.00013, ips: 29.1991 samples/sec | ETA 16:39:53\n",
      "2022-02-27 21:46:10 [INFO]\t[TRAIN] epoch: 1, iter: 4030/150000, loss: 1.4200, lr: 0.009758, batch_cost: 0.4162, reader_cost: 0.00012, ips: 28.8349 samples/sec | ETA 16:52:27\n",
      "2022-02-27 21:46:14 [INFO]\t[TRAIN] epoch: 1, iter: 4040/150000, loss: 1.3495, lr: 0.009757, batch_cost: 0.4242, reader_cost: 0.00015, ips: 28.2853 samples/sec | ETA 17:12:03\n",
      "2022-02-27 21:46:19 [INFO]\t[TRAIN] epoch: 1, iter: 4050/150000, loss: 1.2072, lr: 0.009757, batch_cost: 0.4186, reader_cost: 0.00014, ips: 28.6666 samples/sec | ETA 16:58:15\n",
      "2022-02-27 21:46:23 [INFO]\t[TRAIN] epoch: 1, iter: 4060/150000, loss: 1.2805, lr: 0.009756, batch_cost: 0.4205, reader_cost: 0.00014, ips: 28.5348 samples/sec | ETA 17:02:53\n",
      "2022-02-27 21:46:27 [INFO]\t[TRAIN] epoch: 1, iter: 4070/150000, loss: 1.1764, lr: 0.009756, batch_cost: 0.4163, reader_cost: 0.00013, ips: 28.8269 samples/sec | ETA 16:52:27\n",
      "2022-02-27 21:46:31 [INFO]\t[TRAIN] epoch: 1, iter: 4080/150000, loss: 1.3126, lr: 0.009755, batch_cost: 0.4175, reader_cost: 0.00014, ips: 28.7414 samples/sec | ETA 16:55:24\n",
      "2022-02-27 21:46:35 [INFO]\t[TRAIN] epoch: 1, iter: 4090/150000, loss: 1.2789, lr: 0.009754, batch_cost: 0.4187, reader_cost: 0.00013, ips: 28.6606 samples/sec | ETA 16:58:11\n",
      "2022-02-27 21:46:39 [INFO]\t[TRAIN] epoch: 1, iter: 4100/150000, loss: 1.2486, lr: 0.009754, batch_cost: 0.4149, reader_cost: 0.00015, ips: 28.9205 samples/sec | ETA 16:48:58\n",
      "2022-02-27 21:46:44 [INFO]\t[TRAIN] epoch: 1, iter: 4110/150000, loss: 1.2990, lr: 0.009753, batch_cost: 0.4178, reader_cost: 0.00014, ips: 28.7187 samples/sec | ETA 16:55:59\n",
      "2022-02-27 21:46:48 [INFO]\t[TRAIN] epoch: 1, iter: 4120/150000, loss: 1.1919, lr: 0.009753, batch_cost: 0.4114, reader_cost: 0.00013, ips: 29.1679 samples/sec | ETA 16:40:16\n",
      "2022-02-27 21:46:52 [INFO]\t[TRAIN] epoch: 1, iter: 4130/150000, loss: 1.0355, lr: 0.009752, batch_cost: 0.4192, reader_cost: 0.00013, ips: 28.6246 samples/sec | ETA 16:59:11\n",
      "2022-02-27 21:46:56 [INFO]\t[TRAIN] epoch: 1, iter: 4140/150000, loss: 1.4217, lr: 0.009751, batch_cost: 0.4203, reader_cost: 0.00015, ips: 28.5491 samples/sec | ETA 17:01:49\n",
      "2022-02-27 21:47:00 [INFO]\t[TRAIN] epoch: 1, iter: 4150/150000, loss: 0.9920, lr: 0.009751, batch_cost: 0.4142, reader_cost: 0.00013, ips: 28.9734 samples/sec | ETA 16:46:47\n",
      "2022-02-27 21:47:04 [INFO]\t[TRAIN] epoch: 1, iter: 4160/150000, loss: 1.0611, lr: 0.009750, batch_cost: 0.4145, reader_cost: 0.00013, ips: 28.9516 samples/sec | ETA 16:47:28\n",
      "2022-02-27 21:47:08 [INFO]\t[TRAIN] epoch: 1, iter: 4170/150000, loss: 1.2472, lr: 0.009750, batch_cost: 0.4119, reader_cost: 0.00013, ips: 29.1322 samples/sec | ETA 16:41:09\n",
      "2022-02-27 21:47:13 [INFO]\t[TRAIN] epoch: 1, iter: 4180/150000, loss: 1.2172, lr: 0.009749, batch_cost: 0.4179, reader_cost: 0.00013, ips: 28.7157 samples/sec | ETA 16:55:36\n",
      "2022-02-27 21:47:17 [INFO]\t[TRAIN] epoch: 1, iter: 4190/150000, loss: 1.3903, lr: 0.009748, batch_cost: 0.4208, reader_cost: 0.00014, ips: 28.5194 samples/sec | ETA 17:02:32\n",
      "2022-02-27 21:47:21 [INFO]\t[TRAIN] epoch: 1, iter: 4200/150000, loss: 1.3332, lr: 0.009748, batch_cost: 0.4309, reader_cost: 0.00013, ips: 27.8458 samples/sec | ETA 17:27:11\n",
      "2022-02-27 21:47:25 [INFO]\t[TRAIN] epoch: 1, iter: 4210/150000, loss: 1.4011, lr: 0.009747, batch_cost: 0.4305, reader_cost: 0.00014, ips: 27.8756 samples/sec | ETA 17:26:00\n",
      "2022-02-27 21:47:30 [INFO]\t[TRAIN] epoch: 1, iter: 4220/150000, loss: 1.2193, lr: 0.009747, batch_cost: 0.4201, reader_cost: 0.00013, ips: 28.5623 samples/sec | ETA 17:00:47\n",
      "2022-02-27 21:47:34 [INFO]\t[TRAIN] epoch: 1, iter: 4230/150000, loss: 1.2664, lr: 0.009746, batch_cost: 0.4230, reader_cost: 0.00014, ips: 28.3667 samples/sec | ETA 17:07:45\n",
      "2022-02-27 21:47:38 [INFO]\t[TRAIN] epoch: 1, iter: 4240/150000, loss: 1.2591, lr: 0.009745, batch_cost: 0.4130, reader_cost: 0.00012, ips: 29.0547 samples/sec | ETA 16:43:21\n",
      "2022-02-27 21:47:42 [INFO]\t[TRAIN] epoch: 1, iter: 4250/150000, loss: 1.2619, lr: 0.009745, batch_cost: 0.4168, reader_cost: 0.00013, ips: 28.7917 samples/sec | ETA 16:52:26\n",
      "2022-02-27 21:47:46 [INFO]\t[TRAIN] epoch: 1, iter: 4260/150000, loss: 1.3427, lr: 0.009744, batch_cost: 0.4107, reader_cost: 0.00012, ips: 29.2214 samples/sec | ETA 16:37:29\n",
      "2022-02-27 21:47:50 [INFO]\t[TRAIN] epoch: 1, iter: 4270/150000, loss: 1.2430, lr: 0.009743, batch_cost: 0.4128, reader_cost: 0.00013, ips: 29.0718 samples/sec | ETA 16:42:33\n",
      "2022-02-27 21:47:55 [INFO]\t[TRAIN] epoch: 1, iter: 4280/150000, loss: 1.1789, lr: 0.009743, batch_cost: 0.4162, reader_cost: 0.00013, ips: 28.8346 samples/sec | ETA 16:50:43\n",
      "2022-02-27 21:47:59 [INFO]\t[TRAIN] epoch: 1, iter: 4290/150000, loss: 1.2187, lr: 0.009742, batch_cost: 0.4152, reader_cost: 0.00013, ips: 28.9038 samples/sec | ETA 16:48:14\n",
      "2022-02-27 21:48:03 [INFO]\t[TRAIN] epoch: 1, iter: 4300/150000, loss: 1.1364, lr: 0.009742, batch_cost: 0.4192, reader_cost: 0.00013, ips: 28.6226 samples/sec | ETA 16:58:04\n",
      "2022-02-27 21:48:07 [INFO]\t[TRAIN] epoch: 1, iter: 4310/150000, loss: 1.2372, lr: 0.009741, batch_cost: 0.4184, reader_cost: 0.00013, ips: 28.6823 samples/sec | ETA 16:55:53\n",
      "2022-02-27 21:48:11 [INFO]\t[TRAIN] epoch: 1, iter: 4320/150000, loss: 1.3195, lr: 0.009740, batch_cost: 0.4114, reader_cost: 0.00013, ips: 29.1711 samples/sec | ETA 16:38:47\n",
      "2022-02-27 21:48:16 [INFO]\t[TRAIN] epoch: 1, iter: 4330/150000, loss: 1.2044, lr: 0.009740, batch_cost: 0.4287, reader_cost: 0.00015, ips: 27.9929 samples/sec | ETA 17:20:45\n",
      "2022-02-27 21:48:20 [INFO]\t[TRAIN] epoch: 1, iter: 4340/150000, loss: 1.5292, lr: 0.009739, batch_cost: 0.4230, reader_cost: 0.00013, ips: 28.3677 samples/sec | ETA 17:06:56\n",
      "2022-02-27 21:48:24 [INFO]\t[TRAIN] epoch: 1, iter: 4350/150000, loss: 1.2898, lr: 0.009739, batch_cost: 0.4185, reader_cost: 0.00014, ips: 28.6739 samples/sec | ETA 16:55:54\n",
      "2022-02-27 21:48:28 [INFO]\t[TRAIN] epoch: 1, iter: 4360/150000, loss: 1.4275, lr: 0.009738, batch_cost: 0.4211, reader_cost: 0.00013, ips: 28.4968 samples/sec | ETA 17:02:09\n",
      "2022-02-27 21:48:32 [INFO]\t[TRAIN] epoch: 1, iter: 4370/150000, loss: 1.3413, lr: 0.009737, batch_cost: 0.4274, reader_cost: 0.00014, ips: 28.0791 samples/sec | ETA 17:17:17\n",
      "2022-02-27 21:48:37 [INFO]\t[TRAIN] epoch: 1, iter: 4380/150000, loss: 1.2912, lr: 0.009737, batch_cost: 0.4221, reader_cost: 0.00013, ips: 28.4272 samples/sec | ETA 17:04:30\n",
      "2022-02-27 21:48:41 [INFO]\t[TRAIN] epoch: 1, iter: 4390/150000, loss: 1.2423, lr: 0.009736, batch_cost: 0.4232, reader_cost: 0.00014, ips: 28.3522 samples/sec | ETA 17:07:09\n",
      "2022-02-27 21:48:45 [INFO]\t[TRAIN] epoch: 1, iter: 4400/150000, loss: 1.3434, lr: 0.009736, batch_cost: 0.4227, reader_cost: 0.00014, ips: 28.3869 samples/sec | ETA 17:05:49\n",
      "2022-02-27 21:48:49 [INFO]\t[TRAIN] epoch: 1, iter: 4410/150000, loss: 1.4459, lr: 0.009735, batch_cost: 0.4169, reader_cost: 0.00014, ips: 28.7832 samples/sec | ETA 16:51:37\n",
      "2022-02-27 21:48:54 [INFO]\t[TRAIN] epoch: 1, iter: 4420/150000, loss: 1.4912, lr: 0.009734, batch_cost: 0.4267, reader_cost: 0.00013, ips: 28.1217 samples/sec | ETA 17:15:21\n",
      "2022-02-27 21:48:58 [INFO]\t[TRAIN] epoch: 1, iter: 4430/150000, loss: 1.3186, lr: 0.009734, batch_cost: 0.4180, reader_cost: 0.00013, ips: 28.7064 samples/sec | ETA 16:54:11\n",
      "2022-02-27 21:49:02 [INFO]\t[TRAIN] epoch: 1, iter: 4440/150000, loss: 1.4251, lr: 0.009733, batch_cost: 0.4232, reader_cost: 0.00016, ips: 28.3572 samples/sec | ETA 17:06:37\n",
      "2022-02-27 21:49:06 [INFO]\t[TRAIN] epoch: 1, iter: 4450/150000, loss: 1.4283, lr: 0.009733, batch_cost: 0.4248, reader_cost: 0.00014, ips: 28.2512 samples/sec | ETA 17:10:23\n",
      "2022-02-27 21:49:10 [INFO]\t[TRAIN] epoch: 1, iter: 4460/150000, loss: 1.2713, lr: 0.009732, batch_cost: 0.4181, reader_cost: 0.00013, ips: 28.7032 samples/sec | ETA 16:54:06\n",
      "2022-02-27 21:49:15 [INFO]\t[TRAIN] epoch: 1, iter: 4470/150000, loss: 1.3778, lr: 0.009731, batch_cost: 0.4381, reader_cost: 0.00015, ips: 27.3898 samples/sec | ETA 17:42:39\n",
      "2022-02-27 21:49:19 [INFO]\t[TRAIN] epoch: 1, iter: 4480/150000, loss: 1.4249, lr: 0.009731, batch_cost: 0.4334, reader_cost: 0.00015, ips: 27.6892 samples/sec | ETA 17:31:05\n",
      "2022-02-27 21:49:23 [INFO]\t[TRAIN] epoch: 1, iter: 4490/150000, loss: 1.4672, lr: 0.009730, batch_cost: 0.4217, reader_cost: 0.00014, ips: 28.4569 samples/sec | ETA 17:02:40\n",
      "2022-02-27 21:49:28 [INFO]\t[TRAIN] epoch: 1, iter: 4500/150000, loss: 1.5162, lr: 0.009730, batch_cost: 0.4227, reader_cost: 0.00013, ips: 28.3914 samples/sec | ETA 17:04:57\n",
      "2022-02-27 21:49:32 [INFO]\t[TRAIN] epoch: 1, iter: 4510/150000, loss: 1.3307, lr: 0.009729, batch_cost: 0.4219, reader_cost: 0.00013, ips: 28.4395 samples/sec | ETA 17:03:09\n",
      "2022-02-27 21:49:36 [INFO]\t[TRAIN] epoch: 1, iter: 4520/150000, loss: 1.4145, lr: 0.009728, batch_cost: 0.4285, reader_cost: 0.00014, ips: 28.0063 samples/sec | ETA 17:18:54\n",
      "2022-02-27 21:49:40 [INFO]\t[TRAIN] epoch: 1, iter: 4530/150000, loss: 1.3785, lr: 0.009728, batch_cost: 0.4148, reader_cost: 0.00013, ips: 28.9268 samples/sec | ETA 16:45:46\n",
      "2022-02-27 21:49:44 [INFO]\t[TRAIN] epoch: 1, iter: 4540/150000, loss: 1.3506, lr: 0.009727, batch_cost: 0.4236, reader_cost: 0.00014, ips: 28.3285 samples/sec | ETA 17:06:57\n",
      "2022-02-27 21:49:49 [INFO]\t[TRAIN] epoch: 1, iter: 4550/150000, loss: 1.4374, lr: 0.009727, batch_cost: 0.4214, reader_cost: 0.00014, ips: 28.4755 samples/sec | ETA 17:01:34\n",
      "2022-02-27 21:49:53 [INFO]\t[TRAIN] epoch: 1, iter: 4560/150000, loss: 1.1865, lr: 0.009726, batch_cost: 0.4367, reader_cost: 0.00017, ips: 27.4814 samples/sec | ETA 17:38:27\n",
      "2022-02-27 21:49:57 [INFO]\t[TRAIN] epoch: 1, iter: 4570/150000, loss: 1.1464, lr: 0.009725, batch_cost: 0.4235, reader_cost: 0.00014, ips: 28.3376 samples/sec | ETA 17:06:24\n",
      "2022-02-27 21:50:01 [INFO]\t[TRAIN] epoch: 1, iter: 4580/150000, loss: 1.1741, lr: 0.009725, batch_cost: 0.4195, reader_cost: 0.00014, ips: 28.6058 samples/sec | ETA 16:56:42\n",
      "2022-02-27 21:50:06 [INFO]\t[TRAIN] epoch: 1, iter: 4590/150000, loss: 1.3656, lr: 0.009724, batch_cost: 0.4287, reader_cost: 0.00015, ips: 27.9900 samples/sec | ETA 17:19:00\n",
      "2022-02-27 21:50:10 [INFO]\t[TRAIN] epoch: 1, iter: 4600/150000, loss: 1.6163, lr: 0.009724, batch_cost: 0.4132, reader_cost: 0.00013, ips: 29.0391 samples/sec | ETA 16:41:24\n",
      "2022-02-27 21:50:14 [INFO]\t[TRAIN] epoch: 1, iter: 4610/150000, loss: 1.4554, lr: 0.009723, batch_cost: 0.4169, reader_cost: 0.00013, ips: 28.7872 samples/sec | ETA 16:50:06\n",
      "2022-02-27 21:50:18 [INFO]\t[TRAIN] epoch: 1, iter: 4620/150000, loss: 1.2112, lr: 0.009722, batch_cost: 0.4144, reader_cost: 0.00013, ips: 28.9568 samples/sec | ETA 16:44:07\n",
      "2022-02-27 21:50:22 [INFO]\t[TRAIN] epoch: 1, iter: 4630/150000, loss: 1.3726, lr: 0.009722, batch_cost: 0.4162, reader_cost: 0.00013, ips: 28.8305 samples/sec | ETA 16:48:26\n",
      "2022-02-27 21:50:27 [INFO]\t[TRAIN] epoch: 1, iter: 4640/150000, loss: 1.2436, lr: 0.009721, batch_cost: 0.4205, reader_cost: 0.00014, ips: 28.5397 samples/sec | ETA 16:58:39\n",
      "2022-02-27 21:50:31 [INFO]\t[TRAIN] epoch: 1, iter: 4650/150000, loss: 1.2402, lr: 0.009721, batch_cost: 0.4156, reader_cost: 0.00013, ips: 28.8723 samples/sec | ETA 16:46:50\n",
      "2022-02-27 21:50:35 [INFO]\t[TRAIN] epoch: 1, iter: 4660/150000, loss: 1.4461, lr: 0.009720, batch_cost: 0.4214, reader_cost: 0.00013, ips: 28.4759 samples/sec | ETA 17:00:47\n",
      "2022-02-27 21:50:39 [INFO]\t[TRAIN] epoch: 1, iter: 4670/150000, loss: 1.2697, lr: 0.009719, batch_cost: 0.4389, reader_cost: 0.00014, ips: 27.3429 samples/sec | ETA 17:43:01\n",
      "2022-02-27 21:50:44 [INFO]\t[TRAIN] epoch: 1, iter: 4680/150000, loss: 1.4064, lr: 0.009719, batch_cost: 0.4207, reader_cost: 0.00013, ips: 28.5257 samples/sec | ETA 16:58:52\n",
      "2022-02-27 21:50:48 [INFO]\t[TRAIN] epoch: 1, iter: 4690/150000, loss: 1.2301, lr: 0.009718, batch_cost: 0.4217, reader_cost: 0.00013, ips: 28.4588 samples/sec | ETA 17:01:11\n",
      "2022-02-27 21:50:52 [INFO]\t[TRAIN] epoch: 1, iter: 4700/150000, loss: 1.0986, lr: 0.009718, batch_cost: 0.4314, reader_cost: 0.00013, ips: 27.8181 samples/sec | ETA 17:24:38\n",
      "2022-02-27 21:50:57 [INFO]\t[TRAIN] epoch: 1, iter: 4710/150000, loss: 1.2166, lr: 0.009717, batch_cost: 0.4460, reader_cost: 0.00013, ips: 26.9088 samples/sec | ETA 17:59:52\n",
      "2022-02-27 21:51:01 [INFO]\t[TRAIN] epoch: 1, iter: 4720/150000, loss: 1.2782, lr: 0.009716, batch_cost: 0.4165, reader_cost: 0.00013, ips: 28.8138 samples/sec | ETA 16:48:24\n",
      "2022-02-27 21:51:05 [INFO]\t[TRAIN] epoch: 1, iter: 4730/150000, loss: 1.3188, lr: 0.009716, batch_cost: 0.4180, reader_cost: 0.00013, ips: 28.7054 samples/sec | ETA 16:52:08\n",
      "2022-02-27 21:51:09 [INFO]\t[TRAIN] epoch: 1, iter: 4740/150000, loss: 1.4103, lr: 0.009715, batch_cost: 0.4219, reader_cost: 0.00014, ips: 28.4437 samples/sec | ETA 17:01:23\n",
      "2022-02-27 21:51:13 [INFO]\t[TRAIN] epoch: 1, iter: 4750/150000, loss: 1.3010, lr: 0.009715, batch_cost: 0.4163, reader_cost: 0.00015, ips: 28.8251 samples/sec | ETA 16:47:48\n",
      "2022-02-27 21:51:17 [INFO]\t[TRAIN] epoch: 1, iter: 4760/150000, loss: 1.2250, lr: 0.009714, batch_cost: 0.4123, reader_cost: 0.00013, ips: 29.1033 samples/sec | ETA 16:38:05\n",
      "2022-02-27 21:51:22 [INFO]\t[TRAIN] epoch: 1, iter: 4770/150000, loss: 1.1440, lr: 0.009713, batch_cost: 0.4164, reader_cost: 0.00014, ips: 28.8154 samples/sec | ETA 16:48:00\n",
      "2022-02-27 21:51:26 [INFO]\t[TRAIN] epoch: 1, iter: 4780/150000, loss: 1.2765, lr: 0.009713, batch_cost: 0.4164, reader_cost: 0.00014, ips: 28.8203 samples/sec | ETA 16:47:45\n",
      "2022-02-27 21:51:30 [INFO]\t[TRAIN] epoch: 1, iter: 4790/150000, loss: 1.1985, lr: 0.009712, batch_cost: 0.4206, reader_cost: 0.00015, ips: 28.5320 samples/sec | ETA 16:57:52\n",
      "2022-02-27 21:51:34 [INFO]\t[TRAIN] epoch: 1, iter: 4800/150000, loss: 1.2904, lr: 0.009712, batch_cost: 0.4174, reader_cost: 0.00014, ips: 28.7461 samples/sec | ETA 16:50:13\n",
      "2022-02-27 21:51:38 [INFO]\t[TRAIN] epoch: 1, iter: 4810/150000, loss: 1.2809, lr: 0.009711, batch_cost: 0.4226, reader_cost: 0.00015, ips: 28.3986 samples/sec | ETA 17:02:30\n",
      "2022-02-27 21:51:42 [INFO]\t[TRAIN] epoch: 1, iter: 4820/150000, loss: 1.3862, lr: 0.009710, batch_cost: 0.4152, reader_cost: 0.00013, ips: 28.8997 samples/sec | ETA 16:44:42\n",
      "2022-02-27 21:51:47 [INFO]\t[TRAIN] epoch: 1, iter: 4830/150000, loss: 1.1237, lr: 0.009710, batch_cost: 0.4181, reader_cost: 0.00013, ips: 28.7033 samples/sec | ETA 16:51:31\n",
      "2022-02-27 21:51:51 [INFO]\t[TRAIN] epoch: 1, iter: 4840/150000, loss: 1.3874, lr: 0.009709, batch_cost: 0.4162, reader_cost: 0.00013, ips: 28.8323 samples/sec | ETA 16:46:55\n",
      "2022-02-27 21:51:55 [INFO]\t[TRAIN] epoch: 1, iter: 4850/150000, loss: 1.2091, lr: 0.009709, batch_cost: 0.4152, reader_cost: 0.00013, ips: 28.9007 samples/sec | ETA 16:44:28\n",
      "2022-02-27 21:51:59 [INFO]\t[TRAIN] epoch: 1, iter: 4860/150000, loss: 1.2941, lr: 0.009708, batch_cost: 0.4207, reader_cost: 0.00013, ips: 28.5227 samples/sec | ETA 16:57:42\n",
      "2022-02-27 21:52:03 [INFO]\t[TRAIN] epoch: 1, iter: 4870/150000, loss: 1.2904, lr: 0.009707, batch_cost: 0.4141, reader_cost: 0.00013, ips: 28.9751 samples/sec | ETA 16:41:45\n",
      "2022-02-27 21:52:07 [INFO]\t[TRAIN] epoch: 1, iter: 4880/150000, loss: 1.3780, lr: 0.009707, batch_cost: 0.4157, reader_cost: 0.00014, ips: 28.8697 samples/sec | ETA 16:45:20\n",
      "2022-02-27 21:52:12 [INFO]\t[TRAIN] epoch: 1, iter: 4890/150000, loss: 1.1634, lr: 0.009706, batch_cost: 0.4201, reader_cost: 0.00014, ips: 28.5643 samples/sec | ETA 16:56:01\n",
      "2022-02-27 21:52:16 [INFO]\t[TRAIN] epoch: 1, iter: 4900/150000, loss: 1.3581, lr: 0.009706, batch_cost: 0.4161, reader_cost: 0.00014, ips: 28.8387 samples/sec | ETA 16:46:17\n",
      "2022-02-27 21:52:20 [INFO]\t[TRAIN] epoch: 1, iter: 4910/150000, loss: 1.4389, lr: 0.009705, batch_cost: 0.4216, reader_cost: 0.00013, ips: 28.4635 samples/sec | ETA 16:59:28\n",
      "2022-02-27 21:52:24 [INFO]\t[TRAIN] epoch: 1, iter: 4920/150000, loss: 1.3518, lr: 0.009704, batch_cost: 0.4240, reader_cost: 0.00013, ips: 28.2999 samples/sec | ETA 17:05:18\n",
      "2022-02-27 21:52:29 [INFO]\t[TRAIN] epoch: 1, iter: 4930/150000, loss: 1.1641, lr: 0.009704, batch_cost: 0.4256, reader_cost: 0.00013, ips: 28.1925 samples/sec | ETA 17:09:08\n",
      "2022-02-27 21:52:33 [INFO]\t[TRAIN] epoch: 1, iter: 4940/150000, loss: 1.6567, lr: 0.009703, batch_cost: 0.4308, reader_cost: 0.00014, ips: 27.8547 samples/sec | ETA 17:21:32\n",
      "2022-02-27 21:52:37 [INFO]\t[TRAIN] epoch: 1, iter: 4950/150000, loss: 1.2922, lr: 0.009703, batch_cost: 0.4188, reader_cost: 0.00014, ips: 28.6528 samples/sec | ETA 16:52:27\n",
      "2022-02-27 21:52:41 [INFO]\t[TRAIN] epoch: 1, iter: 4960/150000, loss: 1.1350, lr: 0.009702, batch_cost: 0.4217, reader_cost: 0.00014, ips: 28.4542 samples/sec | ETA 16:59:27\n",
      "2022-02-27 21:52:45 [INFO]\t[TRAIN] epoch: 1, iter: 4970/150000, loss: 1.2442, lr: 0.009701, batch_cost: 0.4201, reader_cost: 0.00013, ips: 28.5641 samples/sec | ETA 16:55:28\n",
      "2022-02-27 21:52:50 [INFO]\t[TRAIN] epoch: 1, iter: 4980/150000, loss: 1.2218, lr: 0.009701, batch_cost: 0.4223, reader_cost: 0.00013, ips: 28.4162 samples/sec | ETA 17:00:41\n",
      "2022-02-27 21:52:54 [INFO]\t[TRAIN] epoch: 1, iter: 4990/150000, loss: 1.2897, lr: 0.009700, batch_cost: 0.4180, reader_cost: 0.00013, ips: 28.7105 samples/sec | ETA 16:50:09\n",
      "2022-02-27 21:52:58 [INFO]\t[TRAIN] epoch: 1, iter: 5000/150000, loss: 1.3019, lr: 0.009700, batch_cost: 0.4322, reader_cost: 0.00013, ips: 27.7639 samples/sec | ETA 17:24:31\n",
      "2022-02-27 21:52:58 [INFO]\tStart evaluating (total_samples: 3333, total_iters: 3333)...\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/math_op_patch.py:253: UserWarning: The dtype of left and right variables are not the same, left dtype is paddle.int32, but right dtype is paddle.bool, the right dtype will convert to paddle.int32\n",
      "  format(lhs_dtype, rhs_dtype, lhs_dtype))\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/math_op_patch.py:253: UserWarning: The dtype of left and right variables are not the same, left dtype is paddle.int64, but right dtype is paddle.bool, the right dtype will convert to paddle.int64\n",
      "  format(lhs_dtype, rhs_dtype, lhs_dtype))\n",
      "3333/3333 [==============================] - 327s 98ms/step - batch_cost: 0.0978 - reader cost: 1.0076e-0411s - batch_cost: 0.0978 - rea - ETA: 10s - batch_cost: 0.0978 - reader cost: 1.0079e- - ETA: 10s - batch_cost: 0.0978 - read - ETA: 9s - batch_cost: 0.0978 - reader cost: 1. - ETA: 8s - batch_cost: 0.0978 - ETA: 5s - batch_cost:  - ETA: 2s - batch_cost: 0.0978  - ETA: 0s - batch_cost: 0.0978 - reader cost: 1.0076e\n",
      "2022-02-27 21:58:25 [INFO]\t[EVAL] #Images: 3333 mIoU: 0.4373 Acc: 0.6472 Kappa: 0.5059 \n",
      "2022-02-27 21:58:25 [INFO]\t[EVAL] Class IoU: \n",
      "[0.4203 0.6769 0.4285 0.2233]\n",
      "2022-02-27 21:58:25 [INFO]\t[EVAL] Class Acc: \n",
      "[0.5355 0.7765 0.5578 0.7533]\n",
      "2022-02-27 21:58:26 [INFO]\t[EVAL] The model with the best validation mIoU (0.4373) was saved at iter 5000.\n",
      "2022-02-27 21:58:31 [INFO]\t[TRAIN] epoch: 1, iter: 5010/150000, loss: 1.3015, lr: 0.009699, batch_cost: 0.4383, reader_cost: 0.00014, ips: 27.3780 samples/sec | ETA 17:39:10\n",
      "2022-02-27 21:58:35 [INFO]\t[TRAIN] epoch: 1, iter: 5020/150000, loss: 1.2485, lr: 0.009698, batch_cost: 0.4206, reader_cost: 0.00013, ips: 28.5318 samples/sec | ETA 16:56:16\n",
      "2022-02-27 21:58:39 [INFO]\t[TRAIN] epoch: 1, iter: 5030/150000, loss: 1.3114, lr: 0.009698, batch_cost: 0.4231, reader_cost: 0.00015, ips: 28.3652 samples/sec | ETA 17:02:10\n",
      "2022-02-27 21:58:43 [INFO]\t[TRAIN] epoch: 1, iter: 5040/150000, loss: 1.4307, lr: 0.009697, batch_cost: 0.4242, reader_cost: 0.00015, ips: 28.2892 samples/sec | ETA 17:04:50\n",
      "2022-02-27 21:58:48 [INFO]\t[TRAIN] epoch: 1, iter: 5050/150000, loss: 1.1445, lr: 0.009697, batch_cost: 0.4216, reader_cost: 0.00014, ips: 28.4608 samples/sec | ETA 16:58:35\n",
      "2022-02-27 21:58:52 [INFO]\t[TRAIN] epoch: 1, iter: 5060/150000, loss: 1.1027, lr: 0.009696, batch_cost: 0.4344, reader_cost: 0.00014, ips: 27.6272 samples/sec | ETA 17:29:15\n",
      "2022-02-27 21:58:56 [INFO]\t[TRAIN] epoch: 1, iter: 5070/150000, loss: 1.1821, lr: 0.009695, batch_cost: 0.4192, reader_cost: 0.00014, ips: 28.6254 samples/sec | ETA 16:52:35\n",
      "2022-02-27 21:59:00 [INFO]\t[TRAIN] epoch: 1, iter: 5080/150000, loss: 1.1455, lr: 0.009695, batch_cost: 0.4181, reader_cost: 0.00013, ips: 28.6997 samples/sec | ETA 16:49:54\n",
      "2022-02-27 21:59:05 [INFO]\t[TRAIN] epoch: 1, iter: 5090/150000, loss: 1.3163, lr: 0.009694, batch_cost: 0.4305, reader_cost: 0.00013, ips: 27.8768 samples/sec | ETA 17:19:38\n",
      "2022-02-27 21:59:09 [INFO]\t[TRAIN] epoch: 1, iter: 5100/150000, loss: 1.3963, lr: 0.009694, batch_cost: 0.4247, reader_cost: 0.00014, ips: 28.2528 samples/sec | ETA 17:05:44\n",
      "2022-02-27 21:59:13 [INFO]\t[TRAIN] epoch: 1, iter: 5110/150000, loss: 1.2580, lr: 0.009693, batch_cost: 0.4326, reader_cost: 0.00014, ips: 27.7396 samples/sec | ETA 17:24:38\n",
      "2022-02-27 21:59:18 [INFO]\t[TRAIN] epoch: 1, iter: 5120/150000, loss: 1.1273, lr: 0.009692, batch_cost: 0.4200, reader_cost: 0.00013, ips: 28.5700 samples/sec | ETA 16:54:12\n",
      "2022-02-27 21:59:22 [INFO]\t[TRAIN] epoch: 1, iter: 5130/150000, loss: 1.2565, lr: 0.009692, batch_cost: 0.4207, reader_cost: 0.00013, ips: 28.5223 samples/sec | ETA 16:55:50\n",
      "2022-02-27 21:59:26 [INFO]\t[TRAIN] epoch: 1, iter: 5140/150000, loss: 1.2974, lr: 0.009691, batch_cost: 0.4296, reader_cost: 0.00014, ips: 27.9347 samples/sec | ETA 17:17:07\n",
      "2022-02-27 21:59:30 [INFO]\t[TRAIN] epoch: 1, iter: 5150/150000, loss: 1.1539, lr: 0.009691, batch_cost: 0.4246, reader_cost: 0.00014, ips: 28.2635 samples/sec | ETA 17:04:59\n",
      "2022-02-27 21:59:34 [INFO]\t[TRAIN] epoch: 1, iter: 5160/150000, loss: 1.1402, lr: 0.009690, batch_cost: 0.4200, reader_cost: 0.00014, ips: 28.5707 samples/sec | ETA 16:53:54\n",
      "2022-02-27 21:59:39 [INFO]\t[TRAIN] epoch: 1, iter: 5170/150000, loss: 1.3170, lr: 0.009689, batch_cost: 0.4180, reader_cost: 0.00013, ips: 28.7075 samples/sec | ETA 16:49:00\n",
      "2022-02-27 21:59:43 [INFO]\t[TRAIN] epoch: 1, iter: 5180/150000, loss: 1.0656, lr: 0.009689, batch_cost: 0.4203, reader_cost: 0.00013, ips: 28.5503 samples/sec | ETA 16:54:29\n",
      "2022-02-27 21:59:47 [INFO]\t[TRAIN] epoch: 1, iter: 5190/150000, loss: 1.2924, lr: 0.009688, batch_cost: 0.4256, reader_cost: 0.00016, ips: 28.1945 samples/sec | ETA 17:07:13\n",
      "2022-02-27 21:59:51 [INFO]\t[TRAIN] epoch: 1, iter: 5200/150000, loss: 1.2975, lr: 0.009688, batch_cost: 0.4262, reader_cost: 0.00015, ips: 28.1578 samples/sec | ETA 17:08:29\n",
      "2022-02-27 21:59:56 [INFO]\t[TRAIN] epoch: 1, iter: 5210/150000, loss: 1.3151, lr: 0.009687, batch_cost: 0.4289, reader_cost: 0.00014, ips: 27.9814 samples/sec | ETA 17:14:54\n",
      "2022-02-27 22:00:00 [INFO]\t[TRAIN] epoch: 1, iter: 5220/150000, loss: 1.4106, lr: 0.009686, batch_cost: 0.4180, reader_cost: 0.00013, ips: 28.7105 samples/sec | ETA 16:48:33\n",
      "2022-02-27 22:00:04 [INFO]\t[TRAIN] epoch: 1, iter: 5230/150000, loss: 1.2923, lr: 0.009686, batch_cost: 0.4172, reader_cost: 0.00014, ips: 28.7623 samples/sec | ETA 16:46:39\n",
      "2022-02-27 22:00:08 [INFO]\t[TRAIN] epoch: 1, iter: 5240/150000, loss: 1.3647, lr: 0.009685, batch_cost: 0.4191, reader_cost: 0.00014, ips: 28.6314 samples/sec | ETA 16:51:11\n",
      "2022-02-27 22:00:12 [INFO]\t[TRAIN] epoch: 1, iter: 5250/150000, loss: 1.2417, lr: 0.009685, batch_cost: 0.4194, reader_cost: 0.00014, ips: 28.6122 samples/sec | ETA 16:51:48\n",
      "2022-02-27 22:00:17 [INFO]\t[TRAIN] epoch: 1, iter: 5260/150000, loss: 1.2433, lr: 0.009684, batch_cost: 0.4215, reader_cost: 0.00014, ips: 28.4713 samples/sec | ETA 16:56:44\n",
      "2022-02-27 22:00:21 [INFO]\t[TRAIN] epoch: 1, iter: 5270/150000, loss: 1.4444, lr: 0.009683, batch_cost: 0.4227, reader_cost: 0.00014, ips: 28.3876 samples/sec | ETA 16:59:40\n",
      "2022-02-27 22:00:25 [INFO]\t[TRAIN] epoch: 2, iter: 5280/150000, loss: 0.9838, lr: 0.009683, batch_cost: 0.4295, reader_cost: 0.01942, ips: 27.9366 samples/sec | ETA 17:16:03\n",
      "2022-02-27 22:00:29 [INFO]\t[TRAIN] epoch: 2, iter: 5290/150000, loss: 1.1856, lr: 0.009682, batch_cost: 0.4350, reader_cost: 0.00014, ips: 27.5892 samples/sec | ETA 17:29:01\n",
      "2022-02-27 22:00:34 [INFO]\t[TRAIN] epoch: 2, iter: 5300/150000, loss: 1.1207, lr: 0.009681, batch_cost: 0.4284, reader_cost: 0.00014, ips: 28.0097 samples/sec | ETA 17:13:12\n",
      "2022-02-27 22:00:38 [INFO]\t[TRAIN] epoch: 2, iter: 5310/150000, loss: 1.1739, lr: 0.009681, batch_cost: 0.4424, reader_cost: 0.00015, ips: 27.1227 samples/sec | ETA 17:46:55\n",
      "2022-02-27 22:00:42 [INFO]\t[TRAIN] epoch: 2, iter: 5320/150000, loss: 1.3617, lr: 0.009680, batch_cost: 0.4174, reader_cost: 0.00013, ips: 28.7526 samples/sec | ETA 16:46:22\n",
      "2022-02-27 22:00:47 [INFO]\t[TRAIN] epoch: 2, iter: 5330/150000, loss: 1.5210, lr: 0.009680, batch_cost: 0.4224, reader_cost: 0.00013, ips: 28.4086 samples/sec | ETA 16:58:29\n",
      "2022-02-27 22:00:51 [INFO]\t[TRAIN] epoch: 2, iter: 5340/150000, loss: 1.2255, lr: 0.009679, batch_cost: 0.4283, reader_cost: 0.00014, ips: 28.0200 samples/sec | ETA 17:12:32\n",
      "2022-02-27 22:00:55 [INFO]\t[TRAIN] epoch: 2, iter: 5350/150000, loss: 1.3114, lr: 0.009678, batch_cost: 0.4230, reader_cost: 0.00014, ips: 28.3705 samples/sec | ETA 16:59:43\n",
      "2022-02-27 22:00:59 [INFO]\t[TRAIN] epoch: 2, iter: 5360/150000, loss: 1.3090, lr: 0.009678, batch_cost: 0.4252, reader_cost: 0.00013, ips: 28.2222 samples/sec | ETA 17:05:00\n",
      "2022-02-27 22:01:04 [INFO]\t[TRAIN] epoch: 2, iter: 5370/150000, loss: 1.1908, lr: 0.009677, batch_cost: 0.4280, reader_cost: 0.00014, ips: 28.0362 samples/sec | ETA 17:11:44\n",
      "2022-02-27 22:01:08 [INFO]\t[TRAIN] epoch: 2, iter: 5380/150000, loss: 1.1899, lr: 0.009677, batch_cost: 0.4206, reader_cost: 0.00013, ips: 28.5338 samples/sec | ETA 16:53:40\n",
      "2022-02-27 22:01:12 [INFO]\t[TRAIN] epoch: 2, iter: 5390/150000, loss: 1.2326, lr: 0.009676, batch_cost: 0.4183, reader_cost: 0.00013, ips: 28.6874 samples/sec | ETA 16:48:10\n",
      "2022-02-27 22:01:16 [INFO]\t[TRAIN] epoch: 2, iter: 5400/150000, loss: 1.2968, lr: 0.009675, batch_cost: 0.4191, reader_cost: 0.00014, ips: 28.6332 samples/sec | ETA 16:50:00\n",
      "2022-02-27 22:01:20 [INFO]\t[TRAIN] epoch: 2, iter: 5410/150000, loss: 1.2988, lr: 0.009675, batch_cost: 0.4159, reader_cost: 0.00013, ips: 28.8503 samples/sec | ETA 16:42:20\n",
      "2022-02-27 22:01:25 [INFO]\t[TRAIN] epoch: 2, iter: 5420/150000, loss: 1.1943, lr: 0.009674, batch_cost: 0.4192, reader_cost: 0.00013, ips: 28.6275 samples/sec | ETA 16:50:04\n",
      "2022-02-27 22:01:29 [INFO]\t[TRAIN] epoch: 2, iter: 5430/150000, loss: 1.1963, lr: 0.009674, batch_cost: 0.4162, reader_cost: 0.00014, ips: 28.8348 samples/sec | ETA 16:42:44\n",
      "2022-02-27 22:01:33 [INFO]\t[TRAIN] epoch: 2, iter: 5440/150000, loss: 1.4170, lr: 0.009673, batch_cost: 0.4207, reader_cost: 0.00014, ips: 28.5242 samples/sec | ETA 16:53:35\n",
      "2022-02-27 22:01:37 [INFO]\t[TRAIN] epoch: 2, iter: 5450/150000, loss: 1.2353, lr: 0.009672, batch_cost: 0.4189, reader_cost: 0.00015, ips: 28.6495 samples/sec | ETA 16:49:05\n",
      "2022-02-27 22:01:41 [INFO]\t[TRAIN] epoch: 2, iter: 5460/150000, loss: 1.2997, lr: 0.009672, batch_cost: 0.4192, reader_cost: 0.00014, ips: 28.6244 samples/sec | ETA 16:49:54\n",
      "2022-02-27 22:01:46 [INFO]\t[TRAIN] epoch: 2, iter: 5470/150000, loss: 1.1474, lr: 0.009671, batch_cost: 0.4181, reader_cost: 0.00013, ips: 28.7030 samples/sec | ETA 16:47:04\n",
      "2022-02-27 22:01:50 [INFO]\t[TRAIN] epoch: 2, iter: 5480/150000, loss: 1.1131, lr: 0.009671, batch_cost: 0.4233, reader_cost: 0.00014, ips: 28.3475 samples/sec | ETA 16:59:37\n",
      "2022-02-27 22:01:54 [INFO]\t[TRAIN] epoch: 2, iter: 5490/150000, loss: 1.0216, lr: 0.009670, batch_cost: 0.4235, reader_cost: 0.00014, ips: 28.3347 samples/sec | ETA 17:00:01\n",
      "2022-02-27 22:01:58 [INFO]\t[TRAIN] epoch: 2, iter: 5500/150000, loss: 1.1516, lr: 0.009669, batch_cost: 0.4154, reader_cost: 0.00014, ips: 28.8890 samples/sec | ETA 16:40:22\n",
      "2022-02-27 22:02:02 [INFO]\t[TRAIN] epoch: 2, iter: 5510/150000, loss: 1.2116, lr: 0.009669, batch_cost: 0.4160, reader_cost: 0.00012, ips: 28.8485 samples/sec | ETA 16:41:42\n",
      "2022-02-27 22:02:06 [INFO]\t[TRAIN] epoch: 2, iter: 5520/150000, loss: 1.2218, lr: 0.009668, batch_cost: 0.4183, reader_cost: 0.00014, ips: 28.6846 samples/sec | ETA 16:47:22\n",
      "2022-02-27 22:02:11 [INFO]\t[TRAIN] epoch: 2, iter: 5530/150000, loss: 1.2547, lr: 0.009668, batch_cost: 0.4197, reader_cost: 0.00013, ips: 28.5917 samples/sec | ETA 16:50:34\n",
      "2022-02-27 22:02:15 [INFO]\t[TRAIN] epoch: 2, iter: 5540/150000, loss: 1.0529, lr: 0.009667, batch_cost: 0.4237, reader_cost: 0.00014, ips: 28.3197 samples/sec | ETA 17:00:12\n",
      "2022-02-27 22:02:19 [INFO]\t[TRAIN] epoch: 2, iter: 5550/150000, loss: 1.4076, lr: 0.009666, batch_cost: 0.4295, reader_cost: 0.00014, ips: 27.9370 samples/sec | ETA 17:14:06\n",
      "2022-02-27 22:02:24 [INFO]\t[TRAIN] epoch: 2, iter: 5560/150000, loss: 1.2138, lr: 0.009666, batch_cost: 0.4371, reader_cost: 0.00013, ips: 27.4509 samples/sec | ETA 17:32:20\n",
      "2022-02-27 22:02:28 [INFO]\t[TRAIN] epoch: 2, iter: 5570/150000, loss: 1.3657, lr: 0.009665, batch_cost: 0.4322, reader_cost: 0.00013, ips: 27.7632 samples/sec | ETA 17:20:26\n",
      "2022-02-27 22:02:32 [INFO]\t[TRAIN] epoch: 2, iter: 5580/150000, loss: 1.4863, lr: 0.009665, batch_cost: 0.4225, reader_cost: 0.00014, ips: 28.3998 samples/sec | ETA 16:57:03\n",
      "2022-02-27 22:02:36 [INFO]\t[TRAIN] epoch: 2, iter: 5590/150000, loss: 1.2482, lr: 0.009664, batch_cost: 0.4298, reader_cost: 0.00014, ips: 27.9215 samples/sec | ETA 17:14:24\n",
      "2022-02-27 22:02:41 [INFO]\t[TRAIN] epoch: 2, iter: 5600/150000, loss: 1.1845, lr: 0.009663, batch_cost: 0.4179, reader_cost: 0.00013, ips: 28.7152 samples/sec | ETA 16:45:44\n",
      "2022-02-27 22:02:45 [INFO]\t[TRAIN] epoch: 2, iter: 5610/150000, loss: 1.2592, lr: 0.009663, batch_cost: 0.4169, reader_cost: 0.00015, ips: 28.7813 samples/sec | ETA 16:43:21\n",
      "2022-02-27 22:02:49 [INFO]\t[TRAIN] epoch: 2, iter: 5620/150000, loss: 1.3924, lr: 0.009662, batch_cost: 0.4118, reader_cost: 0.00013, ips: 29.1378 samples/sec | ETA 16:31:01\n",
      "2022-02-27 22:02:53 [INFO]\t[TRAIN] epoch: 2, iter: 5630/150000, loss: 1.1493, lr: 0.009662, batch_cost: 0.4198, reader_cost: 0.00014, ips: 28.5882 samples/sec | ETA 16:49:59\n",
      "2022-02-27 22:02:57 [INFO]\t[TRAIN] epoch: 2, iter: 5640/150000, loss: 1.1694, lr: 0.009661, batch_cost: 0.4198, reader_cost: 0.00015, ips: 28.5854 samples/sec | ETA 16:50:01\n",
      "2022-02-27 22:03:02 [INFO]\t[TRAIN] epoch: 2, iter: 5650/150000, loss: 1.0926, lr: 0.009660, batch_cost: 0.4231, reader_cost: 0.00014, ips: 28.3595 samples/sec | ETA 16:58:00\n",
      "2022-02-27 22:03:06 [INFO]\t[TRAIN] epoch: 2, iter: 5660/150000, loss: 1.1836, lr: 0.009660, batch_cost: 0.4234, reader_cost: 0.00014, ips: 28.3433 samples/sec | ETA 16:58:30\n",
      "2022-02-27 22:03:10 [INFO]\t[TRAIN] epoch: 2, iter: 5670/150000, loss: 1.2468, lr: 0.009659, batch_cost: 0.4220, reader_cost: 0.00015, ips: 28.4358 samples/sec | ETA 16:55:07\n",
      "2022-02-27 22:03:14 [INFO]\t[TRAIN] epoch: 2, iter: 5680/150000, loss: 1.4922, lr: 0.009659, batch_cost: 0.4234, reader_cost: 0.00013, ips: 28.3402 samples/sec | ETA 16:58:28\n",
      "2022-02-27 22:03:18 [INFO]\t[TRAIN] epoch: 2, iter: 5690/150000, loss: 1.2598, lr: 0.009658, batch_cost: 0.4265, reader_cost: 0.00013, ips: 28.1359 samples/sec | ETA 17:05:48\n",
      "2022-02-27 22:03:23 [INFO]\t[TRAIN] epoch: 2, iter: 5700/150000, loss: 1.3042, lr: 0.009657, batch_cost: 0.4146, reader_cost: 0.00014, ips: 28.9447 samples/sec | ETA 16:37:04\n",
      "2022-02-27 22:03:27 [INFO]\t[TRAIN] epoch: 2, iter: 5710/150000, loss: 1.2591, lr: 0.009657, batch_cost: 0.4188, reader_cost: 0.00013, ips: 28.6558 samples/sec | ETA 16:47:03\n",
      "2022-02-27 22:03:31 [INFO]\t[TRAIN] epoch: 2, iter: 5720/150000, loss: 1.1078, lr: 0.009656, batch_cost: 0.4190, reader_cost: 0.00014, ips: 28.6430 samples/sec | ETA 16:47:26\n",
      "2022-02-27 22:03:35 [INFO]\t[TRAIN] epoch: 2, iter: 5730/150000, loss: 1.3286, lr: 0.009656, batch_cost: 0.4329, reader_cost: 0.00014, ips: 27.7196 samples/sec | ETA 17:20:55\n",
      "2022-02-27 22:03:39 [INFO]\t[TRAIN] epoch: 2, iter: 5740/150000, loss: 1.1725, lr: 0.009655, batch_cost: 0.4155, reader_cost: 0.00013, ips: 28.8820 samples/sec | ETA 16:38:57\n",
      "2022-02-27 22:03:44 [INFO]\t[TRAIN] epoch: 2, iter: 5750/150000, loss: 1.1984, lr: 0.009654, batch_cost: 0.4230, reader_cost: 0.00014, ips: 28.3660 samples/sec | ETA 16:57:03\n",
      "2022-02-27 22:03:48 [INFO]\t[TRAIN] epoch: 2, iter: 5760/150000, loss: 1.3492, lr: 0.009654, batch_cost: 0.4179, reader_cost: 0.00015, ips: 28.7136 samples/sec | ETA 16:44:40\n",
      "2022-02-27 22:03:52 [INFO]\t[TRAIN] epoch: 2, iter: 5770/150000, loss: 1.2384, lr: 0.009653, batch_cost: 0.4323, reader_cost: 0.00014, ips: 27.7607 samples/sec | ETA 17:19:05\n",
      "2022-02-27 22:03:57 [INFO]\t[TRAIN] epoch: 2, iter: 5780/150000, loss: 1.3100, lr: 0.009653, batch_cost: 0.4292, reader_cost: 0.00015, ips: 27.9569 samples/sec | ETA 17:11:43\n",
      "2022-02-27 22:04:01 [INFO]\t[TRAIN] epoch: 2, iter: 5790/150000, loss: 1.2751, lr: 0.009652, batch_cost: 0.4239, reader_cost: 0.00015, ips: 28.3109 samples/sec | ETA 16:58:45\n",
      "2022-02-27 22:04:05 [INFO]\t[TRAIN] epoch: 2, iter: 5800/150000, loss: 1.2480, lr: 0.009651, batch_cost: 0.4179, reader_cost: 0.00014, ips: 28.7162 samples/sec | ETA 16:44:18\n",
      "2022-02-27 22:04:09 [INFO]\t[TRAIN] epoch: 2, iter: 5810/150000, loss: 1.2361, lr: 0.009651, batch_cost: 0.4222, reader_cost: 0.00016, ips: 28.4203 samples/sec | ETA 16:54:41\n",
      "2022-02-27 22:04:13 [INFO]\t[TRAIN] epoch: 2, iter: 5820/150000, loss: 1.3977, lr: 0.009650, batch_cost: 0.4196, reader_cost: 0.00015, ips: 28.6006 samples/sec | ETA 16:48:13\n",
      "2022-02-27 22:04:17 [INFO]\t[TRAIN] epoch: 2, iter: 5830/150000, loss: 1.1664, lr: 0.009650, batch_cost: 0.4148, reader_cost: 0.00013, ips: 28.9275 samples/sec | ETA 16:36:46\n",
      "2022-02-27 22:04:22 [INFO]\t[TRAIN] epoch: 2, iter: 5840/150000, loss: 1.3248, lr: 0.009649, batch_cost: 0.4138, reader_cost: 0.00013, ips: 29.0020 samples/sec | ETA 16:34:08\n",
      "2022-02-27 22:04:26 [INFO]\t[TRAIN] epoch: 2, iter: 5850/150000, loss: 1.2369, lr: 0.009648, batch_cost: 0.4285, reader_cost: 0.00014, ips: 28.0077 samples/sec | ETA 17:09:21\n",
      "2022-02-27 22:04:30 [INFO]\t[TRAIN] epoch: 2, iter: 5860/150000, loss: 1.1105, lr: 0.009648, batch_cost: 0.4275, reader_cost: 0.00013, ips: 28.0725 samples/sec | ETA 17:06:54\n",
      "2022-02-27 22:04:34 [INFO]\t[TRAIN] epoch: 2, iter: 5870/150000, loss: 1.2662, lr: 0.009647, batch_cost: 0.4185, reader_cost: 0.00013, ips: 28.6739 samples/sec | ETA 16:45:18\n",
      "2022-02-27 22:04:39 [INFO]\t[TRAIN] epoch: 2, iter: 5880/150000, loss: 1.2252, lr: 0.009647, batch_cost: 0.4238, reader_cost: 0.00014, ips: 28.3142 samples/sec | ETA 16:58:00\n",
      "2022-02-27 22:04:43 [INFO]\t[TRAIN] epoch: 2, iter: 5890/150000, loss: 1.2030, lr: 0.009646, batch_cost: 0.4104, reader_cost: 0.00013, ips: 29.2411 samples/sec | ETA 16:25:39\n",
      "2022-02-27 22:04:47 [INFO]\t[TRAIN] epoch: 2, iter: 5900/150000, loss: 1.2408, lr: 0.009645, batch_cost: 0.4136, reader_cost: 0.00013, ips: 29.0122 samples/sec | ETA 16:33:22\n",
      "2022-02-27 22:04:51 [INFO]\t[TRAIN] epoch: 2, iter: 5910/150000, loss: 1.2488, lr: 0.009645, batch_cost: 0.4184, reader_cost: 0.00013, ips: 28.6828 samples/sec | ETA 16:44:42\n",
      "2022-02-27 22:04:55 [INFO]\t[TRAIN] epoch: 2, iter: 5920/150000, loss: 1.1368, lr: 0.009644, batch_cost: 0.4142, reader_cost: 0.00013, ips: 28.9735 samples/sec | ETA 16:34:33\n",
      "2022-02-27 22:04:59 [INFO]\t[TRAIN] epoch: 2, iter: 5930/150000, loss: 1.3682, lr: 0.009644, batch_cost: 0.4215, reader_cost: 0.00015, ips: 28.4703 samples/sec | ETA 16:52:04\n",
      "2022-02-27 22:05:04 [INFO]\t[TRAIN] epoch: 2, iter: 5940/150000, loss: 1.3283, lr: 0.009643, batch_cost: 0.4146, reader_cost: 0.00015, ips: 28.9419 samples/sec | ETA 16:35:30\n",
      "2022-02-27 22:05:08 [INFO]\t[TRAIN] epoch: 2, iter: 5950/150000, loss: 1.3480, lr: 0.009642, batch_cost: 0.4177, reader_cost: 0.00013, ips: 28.7316 samples/sec | ETA 16:42:43\n",
      "2022-02-27 22:05:12 [INFO]\t[TRAIN] epoch: 2, iter: 5960/150000, loss: 1.3195, lr: 0.009642, batch_cost: 0.4106, reader_cost: 0.00012, ips: 29.2285 samples/sec | ETA 16:25:36\n",
      "2022-02-27 22:05:16 [INFO]\t[TRAIN] epoch: 2, iter: 5970/150000, loss: 1.3614, lr: 0.009641, batch_cost: 0.4181, reader_cost: 0.00013, ips: 28.7021 samples/sec | ETA 16:43:37\n",
      "2022-02-27 22:05:20 [INFO]\t[TRAIN] epoch: 2, iter: 5980/150000, loss: 1.1698, lr: 0.009641, batch_cost: 0.4125, reader_cost: 0.00014, ips: 29.0933 samples/sec | ETA 16:30:03\n",
      "2022-02-27 22:05:24 [INFO]\t[TRAIN] epoch: 2, iter: 5990/150000, loss: 1.2662, lr: 0.009640, batch_cost: 0.4180, reader_cost: 0.00013, ips: 28.7058 samples/sec | ETA 16:43:20\n",
      "2022-02-27 22:05:29 [INFO]\t[TRAIN] epoch: 2, iter: 6000/150000, loss: 1.1364, lr: 0.009639, batch_cost: 0.4348, reader_cost: 0.00014, ips: 27.5983 samples/sec | ETA 17:23:32\n",
      "2022-02-27 22:05:33 [INFO]\t[TRAIN] epoch: 2, iter: 6010/150000, loss: 1.2501, lr: 0.009639, batch_cost: 0.4272, reader_cost: 0.00013, ips: 28.0891 samples/sec | ETA 17:05:14\n",
      "2022-02-27 22:05:37 [INFO]\t[TRAIN] epoch: 2, iter: 6020/150000, loss: 1.0679, lr: 0.009638, batch_cost: 0.4322, reader_cost: 0.00014, ips: 27.7637 samples/sec | ETA 17:17:10\n",
      "2022-02-27 22:05:42 [INFO]\t[TRAIN] epoch: 2, iter: 6030/150000, loss: 1.2801, lr: 0.009638, batch_cost: 0.4368, reader_cost: 0.00015, ips: 27.4714 samples/sec | ETA 17:28:08\n",
      "2022-02-27 22:05:46 [INFO]\t[TRAIN] epoch: 2, iter: 6040/150000, loss: 1.3615, lr: 0.009637, batch_cost: 0.4156, reader_cost: 0.00013, ips: 28.8725 samples/sec | ETA 16:37:12\n",
      "2022-02-27 22:05:50 [INFO]\t[TRAIN] epoch: 2, iter: 6050/150000, loss: 1.2841, lr: 0.009636, batch_cost: 0.4193, reader_cost: 0.00014, ips: 28.6182 samples/sec | ETA 16:46:00\n",
      "2022-02-27 22:05:54 [INFO]\t[TRAIN] epoch: 2, iter: 6060/150000, loss: 1.0731, lr: 0.009636, batch_cost: 0.4164, reader_cost: 0.00013, ips: 28.8216 samples/sec | ETA 16:38:50\n",
      "2022-02-27 22:05:58 [INFO]\t[TRAIN] epoch: 2, iter: 6070/150000, loss: 1.2393, lr: 0.009635, batch_cost: 0.4243, reader_cost: 0.00014, ips: 28.2823 samples/sec | ETA 16:57:48\n",
      "2022-02-27 22:06:03 [INFO]\t[TRAIN] epoch: 2, iter: 6080/150000, loss: 1.1655, lr: 0.009635, batch_cost: 0.4191, reader_cost: 0.00016, ips: 28.6300 samples/sec | ETA 16:45:22\n",
      "2022-02-27 22:06:07 [INFO]\t[TRAIN] epoch: 2, iter: 6090/150000, loss: 1.4361, lr: 0.009634, batch_cost: 0.4266, reader_cost: 0.00013, ips: 28.1274 samples/sec | ETA 17:03:16\n",
      "2022-02-27 22:06:11 [INFO]\t[TRAIN] epoch: 2, iter: 6100/150000, loss: 1.3546, lr: 0.009633, batch_cost: 0.4174, reader_cost: 0.00013, ips: 28.7485 samples/sec | ETA 16:41:05\n",
      "2022-02-27 22:06:15 [INFO]\t[TRAIN] epoch: 2, iter: 6110/150000, loss: 1.4354, lr: 0.009633, batch_cost: 0.4165, reader_cost: 0.00014, ips: 28.8107 samples/sec | ETA 16:38:51\n",
      "2022-02-27 22:06:19 [INFO]\t[TRAIN] epoch: 2, iter: 6120/150000, loss: 1.3403, lr: 0.009632, batch_cost: 0.4207, reader_cost: 0.00014, ips: 28.5220 samples/sec | ETA 16:48:54\n",
      "2022-02-27 22:06:24 [INFO]\t[TRAIN] epoch: 2, iter: 6130/150000, loss: 1.3482, lr: 0.009631, batch_cost: 0.4159, reader_cost: 0.00013, ips: 28.8556 samples/sec | ETA 16:37:10\n",
      "2022-02-27 22:06:28 [INFO]\t[TRAIN] epoch: 2, iter: 6140/150000, loss: 1.3304, lr: 0.009631, batch_cost: 0.4364, reader_cost: 0.00015, ips: 27.4950 samples/sec | ETA 17:26:26\n",
      "2022-02-27 22:06:32 [INFO]\t[TRAIN] epoch: 2, iter: 6150/150000, loss: 1.2647, lr: 0.009630, batch_cost: 0.4238, reader_cost: 0.00013, ips: 28.3151 samples/sec | ETA 16:56:04\n",
      "2022-02-27 22:06:36 [INFO]\t[TRAIN] epoch: 2, iter: 6160/150000, loss: 1.2500, lr: 0.009630, batch_cost: 0.4206, reader_cost: 0.00014, ips: 28.5300 samples/sec | ETA 16:48:20\n",
      "2022-02-27 22:06:40 [INFO]\t[TRAIN] epoch: 2, iter: 6170/150000, loss: 1.2166, lr: 0.009629, batch_cost: 0.4114, reader_cost: 0.00013, ips: 29.1666 samples/sec | ETA 16:26:15\n",
      "2022-02-27 22:06:45 [INFO]\t[TRAIN] epoch: 2, iter: 6180/150000, loss: 1.0741, lr: 0.009628, batch_cost: 0.4331, reader_cost: 0.00015, ips: 27.7041 samples/sec | ETA 17:18:15\n",
      "2022-02-27 22:06:49 [INFO]\t[TRAIN] epoch: 2, iter: 6190/150000, loss: 1.1788, lr: 0.009628, batch_cost: 0.4248, reader_cost: 0.00050, ips: 28.2473 samples/sec | ETA 16:58:13\n",
      "2022-02-27 22:06:53 [INFO]\t[TRAIN] epoch: 2, iter: 6200/150000, loss: 1.3953, lr: 0.009627, batch_cost: 0.4155, reader_cost: 0.00013, ips: 28.8802 samples/sec | ETA 16:35:50\n",
      "2022-02-27 22:06:57 [INFO]\t[TRAIN] epoch: 2, iter: 6210/150000, loss: 1.2180, lr: 0.009627, batch_cost: 0.4200, reader_cost: 0.00014, ips: 28.5699 samples/sec | ETA 16:46:35\n",
      "2022-02-27 22:07:02 [INFO]\t[TRAIN] epoch: 2, iter: 6220/150000, loss: 1.3277, lr: 0.009626, batch_cost: 0.4242, reader_cost: 0.00014, ips: 28.2903 samples/sec | ETA 16:56:27\n",
      "2022-02-27 22:07:06 [INFO]\t[TRAIN] epoch: 2, iter: 6230/150000, loss: 1.0498, lr: 0.009625, batch_cost: 0.4341, reader_cost: 0.00016, ips: 27.6436 samples/sec | ETA 17:20:10\n",
      "2022-02-27 22:07:10 [INFO]\t[TRAIN] epoch: 2, iter: 6240/150000, loss: 1.2279, lr: 0.009625, batch_cost: 0.4195, reader_cost: 0.00014, ips: 28.6084 samples/sec | ETA 16:45:01\n",
      "2022-02-27 22:07:14 [INFO]\t[TRAIN] epoch: 2, iter: 6250/150000, loss: 1.2753, lr: 0.009624, batch_cost: 0.4209, reader_cost: 0.00014, ips: 28.5133 samples/sec | ETA 16:48:18\n",
      "2022-02-27 22:07:19 [INFO]\t[TRAIN] epoch: 2, iter: 6260/150000, loss: 1.3370, lr: 0.009624, batch_cost: 0.4220, reader_cost: 0.00014, ips: 28.4340 samples/sec | ETA 16:51:02\n",
      "2022-02-27 22:07:23 [INFO]\t[TRAIN] epoch: 2, iter: 6270/150000, loss: 1.2835, lr: 0.009623, batch_cost: 0.4254, reader_cost: 0.00014, ips: 28.2101 samples/sec | ETA 16:58:59\n",
      "2022-02-27 22:07:27 [INFO]\t[TRAIN] epoch: 2, iter: 6280/150000, loss: 1.1491, lr: 0.009622, batch_cost: 0.4294, reader_cost: 0.00014, ips: 27.9437 samples/sec | ETA 17:08:38\n",
      "2022-02-27 22:07:31 [INFO]\t[TRAIN] epoch: 2, iter: 6290/150000, loss: 1.1645, lr: 0.009622, batch_cost: 0.4235, reader_cost: 0.00014, ips: 28.3340 samples/sec | ETA 16:54:24\n",
      "2022-02-27 22:07:36 [INFO]\t[TRAIN] epoch: 2, iter: 6300/150000, loss: 1.1517, lr: 0.009621, batch_cost: 0.4185, reader_cost: 0.00014, ips: 28.6730 samples/sec | ETA 16:42:20\n",
      "2022-02-27 22:07:40 [INFO]\t[TRAIN] epoch: 2, iter: 6310/150000, loss: 1.4091, lr: 0.009621, batch_cost: 0.4322, reader_cost: 0.00013, ips: 27.7628 samples/sec | ETA 17:15:07\n",
      "2022-02-27 22:07:44 [INFO]\t[TRAIN] epoch: 2, iter: 6320/150000, loss: 1.2548, lr: 0.009620, batch_cost: 0.4321, reader_cost: 0.00014, ips: 27.7711 samples/sec | ETA 17:14:44\n",
      "2022-02-27 22:07:49 [INFO]\t[TRAIN] epoch: 2, iter: 6330/150000, loss: 1.2146, lr: 0.009619, batch_cost: 0.4292, reader_cost: 0.00014, ips: 27.9590 samples/sec | ETA 17:07:43\n",
      "2022-02-27 22:07:53 [INFO]\t[TRAIN] epoch: 2, iter: 6340/150000, loss: 1.2276, lr: 0.009619, batch_cost: 0.4269, reader_cost: 0.00013, ips: 28.1082 samples/sec | ETA 17:02:11\n",
      "2022-02-27 22:07:57 [INFO]\t[TRAIN] epoch: 2, iter: 6350/150000, loss: 1.1495, lr: 0.009618, batch_cost: 0.4222, reader_cost: 0.00013, ips: 28.4239 samples/sec | ETA 16:50:46\n",
      "2022-02-27 22:08:01 [INFO]\t[TRAIN] epoch: 2, iter: 6360/150000, loss: 1.2909, lr: 0.009618, batch_cost: 0.4304, reader_cost: 0.00013, ips: 27.8782 samples/sec | ETA 17:10:28\n",
      "2022-02-27 22:08:06 [INFO]\t[TRAIN] epoch: 2, iter: 6370/150000, loss: 1.1164, lr: 0.009617, batch_cost: 0.4194, reader_cost: 0.00015, ips: 28.6091 samples/sec | ETA 16:44:05\n",
      "2022-02-27 22:08:10 [INFO]\t[TRAIN] epoch: 2, iter: 6380/150000, loss: 1.0824, lr: 0.009616, batch_cost: 0.4157, reader_cost: 0.00015, ips: 28.8676 samples/sec | ETA 16:35:01\n",
      "2022-02-27 22:08:14 [INFO]\t[TRAIN] epoch: 2, iter: 6390/150000, loss: 1.0936, lr: 0.009616, batch_cost: 0.4212, reader_cost: 0.00015, ips: 28.4922 samples/sec | ETA 16:48:03\n",
      "2022-02-27 22:08:18 [INFO]\t[TRAIN] epoch: 2, iter: 6400/150000, loss: 0.9953, lr: 0.009615, batch_cost: 0.4147, reader_cost: 0.00015, ips: 28.9351 samples/sec | ETA 16:32:33\n",
      "2022-02-27 22:08:22 [INFO]\t[TRAIN] epoch: 2, iter: 6410/150000, loss: 1.3640, lr: 0.009615, batch_cost: 0.4170, reader_cost: 0.00014, ips: 28.7746 samples/sec | ETA 16:38:01\n",
      "2022-02-27 22:08:26 [INFO]\t[TRAIN] epoch: 2, iter: 6420/150000, loss: 1.3198, lr: 0.009614, batch_cost: 0.4139, reader_cost: 0.00013, ips: 28.9914 samples/sec | ETA 16:30:30\n",
      "2022-02-27 22:08:31 [INFO]\t[TRAIN] epoch: 2, iter: 6430/150000, loss: 1.2948, lr: 0.009613, batch_cost: 0.4206, reader_cost: 0.00013, ips: 28.5320 samples/sec | ETA 16:46:22\n",
      "2022-02-27 22:08:35 [INFO]\t[TRAIN] epoch: 2, iter: 6440/150000, loss: 1.1986, lr: 0.009613, batch_cost: 0.4140, reader_cost: 0.00014, ips: 28.9874 samples/sec | ETA 16:30:30\n",
      "2022-02-27 22:08:39 [INFO]\t[TRAIN] epoch: 2, iter: 6450/150000, loss: 1.2425, lr: 0.009612, batch_cost: 0.4364, reader_cost: 0.00015, ips: 27.4983 samples/sec | ETA 17:24:03\n",
      "2022-02-27 22:08:43 [INFO]\t[TRAIN] epoch: 2, iter: 6460/150000, loss: 1.0114, lr: 0.009612, batch_cost: 0.4332, reader_cost: 0.00013, ips: 27.7029 samples/sec | ETA 17:16:16\n",
      "2022-02-27 22:08:48 [INFO]\t[TRAIN] epoch: 2, iter: 6470/150000, loss: 1.1865, lr: 0.009611, batch_cost: 0.4217, reader_cost: 0.00013, ips: 28.4538 samples/sec | ETA 16:48:51\n",
      "2022-02-27 22:08:52 [INFO]\t[TRAIN] epoch: 2, iter: 6480/150000, loss: 1.2432, lr: 0.009610, batch_cost: 0.4173, reader_cost: 0.00013, ips: 28.7597 samples/sec | ETA 16:38:03\n",
      "2022-02-27 22:08:56 [INFO]\t[TRAIN] epoch: 2, iter: 6490/150000, loss: 1.0225, lr: 0.009610, batch_cost: 0.4165, reader_cost: 0.00014, ips: 28.8112 samples/sec | ETA 16:36:12\n",
      "2022-02-27 22:09:00 [INFO]\t[TRAIN] epoch: 2, iter: 6500/150000, loss: 1.4470, lr: 0.009609, batch_cost: 0.4160, reader_cost: 0.00013, ips: 28.8455 samples/sec | ETA 16:34:57\n",
      "2022-02-27 22:09:04 [INFO]\t[TRAIN] epoch: 2, iter: 6510/150000, loss: 1.1758, lr: 0.009609, batch_cost: 0.4137, reader_cost: 0.00013, ips: 29.0099 samples/sec | ETA 16:29:14\n",
      "2022-02-27 22:09:09 [INFO]\t[TRAIN] epoch: 2, iter: 6520/150000, loss: 1.2409, lr: 0.009608, batch_cost: 0.4242, reader_cost: 0.00015, ips: 28.2859 samples/sec | ETA 16:54:29\n",
      "2022-02-27 22:09:13 [INFO]\t[TRAIN] epoch: 2, iter: 6530/150000, loss: 1.1294, lr: 0.009607, batch_cost: 0.4194, reader_cost: 0.00014, ips: 28.6157 samples/sec | ETA 16:42:44\n",
      "2022-02-27 22:09:17 [INFO]\t[TRAIN] epoch: 2, iter: 6540/150000, loss: 1.2670, lr: 0.009607, batch_cost: 0.4132, reader_cost: 0.00015, ips: 29.0433 samples/sec | ETA 16:27:54\n",
      "2022-02-27 22:09:21 [INFO]\t[TRAIN] epoch: 2, iter: 6550/150000, loss: 1.2219, lr: 0.009606, batch_cost: 0.4163, reader_cost: 0.00013, ips: 28.8247 samples/sec | ETA 16:35:19\n",
      "2022-02-27 22:09:25 [INFO]\t[TRAIN] epoch: 2, iter: 6560/150000, loss: 1.2206, lr: 0.009606, batch_cost: 0.4151, reader_cost: 0.00013, ips: 28.9070 samples/sec | ETA 16:32:25\n",
      "2022-02-27 22:09:29 [INFO]\t[TRAIN] epoch: 2, iter: 6570/150000, loss: 1.2349, lr: 0.009605, batch_cost: 0.4238, reader_cost: 0.00015, ips: 28.3123 samples/sec | ETA 16:53:12\n",
      "2022-02-27 22:09:34 [INFO]\t[TRAIN] epoch: 2, iter: 6580/150000, loss: 1.3336, lr: 0.009604, batch_cost: 0.4142, reader_cost: 0.00013, ips: 28.9733 samples/sec | ETA 16:30:00\n",
      "2022-02-27 22:09:38 [INFO]\t[TRAIN] epoch: 2, iter: 6590/150000, loss: 1.1633, lr: 0.009604, batch_cost: 0.4164, reader_cost: 0.00013, ips: 28.8205 samples/sec | ETA 16:35:11\n",
      "2022-02-27 22:09:42 [INFO]\t[TRAIN] epoch: 2, iter: 6600/150000, loss: 1.0544, lr: 0.009603, batch_cost: 0.4167, reader_cost: 0.00014, ips: 28.7959 samples/sec | ETA 16:35:58\n",
      "2022-02-27 22:09:46 [INFO]\t[TRAIN] epoch: 2, iter: 6610/150000, loss: 1.3301, lr: 0.009603, batch_cost: 0.4198, reader_cost: 0.00013, ips: 28.5828 samples/sec | ETA 16:43:19\n",
      "2022-02-27 22:09:50 [INFO]\t[TRAIN] epoch: 2, iter: 6620/150000, loss: 1.2140, lr: 0.009602, batch_cost: 0.4276, reader_cost: 0.00016, ips: 28.0605 samples/sec | ETA 17:01:56\n",
      "2022-02-27 22:09:55 [INFO]\t[TRAIN] epoch: 2, iter: 6630/150000, loss: 1.2193, lr: 0.009601, batch_cost: 0.4410, reader_cost: 0.00014, ips: 27.2083 samples/sec | ETA 17:33:52\n",
      "2022-02-27 22:09:59 [INFO]\t[TRAIN] epoch: 2, iter: 6640/150000, loss: 1.1695, lr: 0.009601, batch_cost: 0.4530, reader_cost: 0.00014, ips: 26.4892 samples/sec | ETA 18:02:24\n",
      "2022-02-27 22:10:04 [INFO]\t[TRAIN] epoch: 2, iter: 6650/150000, loss: 1.2043, lr: 0.009600, batch_cost: 0.4414, reader_cost: 0.00014, ips: 27.1890 samples/sec | ETA 17:34:28\n",
      "2022-02-27 22:10:08 [INFO]\t[TRAIN] epoch: 2, iter: 6660/150000, loss: 1.2388, lr: 0.009600, batch_cost: 0.4168, reader_cost: 0.00013, ips: 28.7904 samples/sec | ETA 16:35:44\n",
      "2022-02-27 22:10:12 [INFO]\t[TRAIN] epoch: 2, iter: 6670/150000, loss: 1.1685, lr: 0.009599, batch_cost: 0.4221, reader_cost: 0.00015, ips: 28.4272 samples/sec | ETA 16:48:24\n",
      "2022-02-27 22:10:16 [INFO]\t[TRAIN] epoch: 2, iter: 6680/150000, loss: 1.1366, lr: 0.009598, batch_cost: 0.4162, reader_cost: 0.00014, ips: 28.8308 samples/sec | ETA 16:34:12\n",
      "2022-02-27 22:10:20 [INFO]\t[TRAIN] epoch: 2, iter: 6690/150000, loss: 1.2849, lr: 0.009598, batch_cost: 0.4212, reader_cost: 0.00013, ips: 28.4922 samples/sec | ETA 16:45:57\n",
      "2022-02-27 22:10:25 [INFO]\t[TRAIN] epoch: 2, iter: 6700/150000, loss: 1.3161, lr: 0.009597, batch_cost: 0.4158, reader_cost: 0.00013, ips: 28.8580 samples/sec | ETA 16:33:08\n",
      "2022-02-27 22:10:29 [INFO]\t[TRAIN] epoch: 2, iter: 6710/150000, loss: 1.4264, lr: 0.009597, batch_cost: 0.4196, reader_cost: 0.00013, ips: 28.6003 samples/sec | ETA 16:42:00\n",
      "2022-02-27 22:10:33 [INFO]\t[TRAIN] epoch: 2, iter: 6720/150000, loss: 1.1451, lr: 0.009596, batch_cost: 0.4160, reader_cost: 0.00014, ips: 28.8443 samples/sec | ETA 16:33:28\n",
      "2022-02-27 22:10:37 [INFO]\t[TRAIN] epoch: 2, iter: 6730/150000, loss: 1.3175, lr: 0.009595, batch_cost: 0.4127, reader_cost: 0.00013, ips: 29.0780 samples/sec | ETA 16:25:25\n",
      "2022-02-27 22:10:41 [INFO]\t[TRAIN] epoch: 2, iter: 6740/150000, loss: 1.2023, lr: 0.009595, batch_cost: 0.4228, reader_cost: 0.00013, ips: 28.3824 samples/sec | ETA 16:49:29\n",
      "2022-02-27 22:10:46 [INFO]\t[TRAIN] epoch: 2, iter: 6750/150000, loss: 1.2860, lr: 0.009594, batch_cost: 0.4175, reader_cost: 0.00013, ips: 28.7445 samples/sec | ETA 16:36:42\n",
      "2022-02-27 22:10:50 [INFO]\t[TRAIN] epoch: 2, iter: 6760/150000, loss: 1.3848, lr: 0.009594, batch_cost: 0.4312, reader_cost: 0.00015, ips: 27.8315 samples/sec | ETA 17:09:20\n",
      "2022-02-27 22:10:54 [INFO]\t[TRAIN] epoch: 2, iter: 6770/150000, loss: 1.2757, lr: 0.009593, batch_cost: 0.4429, reader_cost: 0.00016, ips: 27.0966 samples/sec | ETA 17:37:10\n",
      "2022-02-27 22:10:58 [INFO]\t[TRAIN] epoch: 2, iter: 6780/150000, loss: 1.1550, lr: 0.009592, batch_cost: 0.4244, reader_cost: 0.00015, ips: 28.2769 samples/sec | ETA 16:52:58\n",
      "2022-02-27 22:11:03 [INFO]\t[TRAIN] epoch: 2, iter: 6790/150000, loss: 0.9574, lr: 0.009592, batch_cost: 0.4193, reader_cost: 0.00014, ips: 28.6192 samples/sec | ETA 16:40:47\n",
      "2022-02-27 22:11:07 [INFO]\t[TRAIN] epoch: 2, iter: 6800/150000, loss: 1.2625, lr: 0.009591, batch_cost: 0.4362, reader_cost: 0.00013, ips: 27.5123 samples/sec | ETA 17:20:59\n",
      "2022-02-27 22:11:11 [INFO]\t[TRAIN] epoch: 2, iter: 6810/150000, loss: 1.2501, lr: 0.009591, batch_cost: 0.4268, reader_cost: 0.00016, ips: 28.1139 samples/sec | ETA 16:58:38\n",
      "2022-02-27 22:11:16 [INFO]\t[TRAIN] epoch: 2, iter: 6820/150000, loss: 1.1184, lr: 0.009590, batch_cost: 0.4208, reader_cost: 0.00014, ips: 28.5186 samples/sec | ETA 16:44:07\n",
      "2022-02-27 22:11:20 [INFO]\t[TRAIN] epoch: 2, iter: 6830/150000, loss: 1.2016, lr: 0.009589, batch_cost: 0.4188, reader_cost: 0.00013, ips: 28.6564 samples/sec | ETA 16:39:13\n",
      "2022-02-27 22:11:24 [INFO]\t[TRAIN] epoch: 2, iter: 6840/150000, loss: 1.3539, lr: 0.009589, batch_cost: 0.4168, reader_cost: 0.00013, ips: 28.7941 samples/sec | ETA 16:34:22\n",
      "2022-02-27 22:11:28 [INFO]\t[TRAIN] epoch: 2, iter: 6850/150000, loss: 1.2710, lr: 0.009588, batch_cost: 0.4173, reader_cost: 0.00013, ips: 28.7539 samples/sec | ETA 16:35:41\n",
      "2022-02-27 22:11:32 [INFO]\t[TRAIN] epoch: 2, iter: 6860/150000, loss: 1.3340, lr: 0.009588, batch_cost: 0.4176, reader_cost: 0.00013, ips: 28.7327 samples/sec | ETA 16:36:21\n",
      "2022-02-27 22:11:36 [INFO]\t[TRAIN] epoch: 2, iter: 6870/150000, loss: 1.4559, lr: 0.009587, batch_cost: 0.4153, reader_cost: 0.00013, ips: 28.8972 samples/sec | ETA 16:30:36\n",
      "2022-02-27 22:11:41 [INFO]\t[TRAIN] epoch: 2, iter: 6880/150000, loss: 1.2069, lr: 0.009586, batch_cost: 0.4185, reader_cost: 0.00014, ips: 28.6711 samples/sec | ETA 16:38:21\n",
      "2022-02-27 22:11:45 [INFO]\t[TRAIN] epoch: 2, iter: 6890/150000, loss: 1.0481, lr: 0.009586, batch_cost: 0.4124, reader_cost: 0.00013, ips: 29.0964 samples/sec | ETA 16:23:41\n",
      "2022-02-27 22:11:49 [INFO]\t[TRAIN] epoch: 2, iter: 6900/150000, loss: 1.1893, lr: 0.009585, batch_cost: 0.4129, reader_cost: 0.00013, ips: 29.0634 samples/sec | ETA 16:24:44\n",
      "2022-02-27 22:11:53 [INFO]\t[TRAIN] epoch: 2, iter: 6910/150000, loss: 1.3509, lr: 0.009584, batch_cost: 0.4198, reader_cost: 0.00014, ips: 28.5853 samples/sec | ETA 16:41:08\n",
      "2022-02-27 22:11:57 [INFO]\t[TRAIN] epoch: 2, iter: 6920/150000, loss: 1.1527, lr: 0.009584, batch_cost: 0.4162, reader_cost: 0.00016, ips: 28.8298 samples/sec | ETA 16:32:34\n",
      "2022-02-27 22:12:01 [INFO]\t[TRAIN] epoch: 2, iter: 6930/150000, loss: 1.2129, lr: 0.009583, batch_cost: 0.4216, reader_cost: 0.00014, ips: 28.4609 samples/sec | ETA 16:45:22\n",
      "2022-02-27 22:12:06 [INFO]\t[TRAIN] epoch: 2, iter: 6940/150000, loss: 1.2682, lr: 0.009583, batch_cost: 0.4432, reader_cost: 0.00014, ips: 27.0747 samples/sec | ETA 17:36:46\n",
      "2022-02-27 22:12:10 [INFO]\t[TRAIN] epoch: 2, iter: 6950/150000, loss: 1.1893, lr: 0.009582, batch_cost: 0.4273, reader_cost: 0.00013, ips: 28.0851 samples/sec | ETA 16:58:41\n",
      "2022-02-27 22:12:14 [INFO]\t[TRAIN] epoch: 2, iter: 6960/150000, loss: 1.1150, lr: 0.009581, batch_cost: 0.4227, reader_cost: 0.00015, ips: 28.3893 samples/sec | ETA 16:47:42\n",
      "2022-02-27 22:12:18 [INFO]\t[TRAIN] epoch: 2, iter: 6970/150000, loss: 1.1538, lr: 0.009581, batch_cost: 0.4159, reader_cost: 0.00014, ips: 28.8510 samples/sec | ETA 16:31:30\n",
      "2022-02-27 22:12:23 [INFO]\t[TRAIN] epoch: 2, iter: 6980/150000, loss: 1.2395, lr: 0.009580, batch_cost: 0.4201, reader_cost: 0.00014, ips: 28.5639 samples/sec | ETA 16:41:24\n",
      "2022-02-27 22:12:27 [INFO]\t[TRAIN] epoch: 2, iter: 6990/150000, loss: 1.0112, lr: 0.009580, batch_cost: 0.4195, reader_cost: 0.00014, ips: 28.6023 samples/sec | ETA 16:39:59\n",
      "2022-02-27 22:12:31 [INFO]\t[TRAIN] epoch: 2, iter: 7000/150000, loss: 1.2953, lr: 0.009579, batch_cost: 0.4260, reader_cost: 0.00013, ips: 28.1697 samples/sec | ETA 16:55:16\n",
      "2022-02-27 22:12:35 [INFO]\t[TRAIN] epoch: 2, iter: 7010/150000, loss: 1.1497, lr: 0.009578, batch_cost: 0.4218, reader_cost: 0.00014, ips: 28.4477 samples/sec | ETA 16:45:17\n",
      "2022-02-27 22:12:40 [INFO]\t[TRAIN] epoch: 2, iter: 7020/150000, loss: 1.4175, lr: 0.009578, batch_cost: 0.4556, reader_cost: 0.00015, ips: 26.3390 samples/sec | ETA 18:05:41\n",
      "2022-02-27 22:12:44 [INFO]\t[TRAIN] epoch: 2, iter: 7030/150000, loss: 1.3819, lr: 0.009577, batch_cost: 0.4260, reader_cost: 0.00013, ips: 28.1666 samples/sec | ETA 16:55:10\n",
      "2022-02-27 22:12:48 [INFO]\t[TRAIN] epoch: 2, iter: 7040/150000, loss: 1.2428, lr: 0.009577, batch_cost: 0.4151, reader_cost: 0.00014, ips: 28.9075 samples/sec | ETA 16:29:05\n",
      "2022-02-27 22:12:53 [INFO]\t[TRAIN] epoch: 2, iter: 7050/150000, loss: 1.1527, lr: 0.009576, batch_cost: 0.4202, reader_cost: 0.00013, ips: 28.5569 samples/sec | ETA 16:41:09\n",
      "2022-02-27 22:12:57 [INFO]\t[TRAIN] epoch: 2, iter: 7060/150000, loss: 1.3534, lr: 0.009575, batch_cost: 0.4398, reader_cost: 0.00015, ips: 27.2864 samples/sec | ETA 17:27:41\n",
      "2022-02-27 22:13:01 [INFO]\t[TRAIN] epoch: 2, iter: 7070/150000, loss: 1.2343, lr: 0.009575, batch_cost: 0.4229, reader_cost: 0.00014, ips: 28.3782 samples/sec | ETA 16:47:19\n",
      "2022-02-27 22:13:05 [INFO]\t[TRAIN] epoch: 2, iter: 7080/150000, loss: 1.1508, lr: 0.009574, batch_cost: 0.4198, reader_cost: 0.00013, ips: 28.5865 samples/sec | ETA 16:39:54\n",
      "2022-02-27 22:13:10 [INFO]\t[TRAIN] epoch: 2, iter: 7090/150000, loss: 1.5154, lr: 0.009574, batch_cost: 0.4173, reader_cost: 0.00013, ips: 28.7555 samples/sec | ETA 16:33:57\n",
      "2022-02-27 22:13:14 [INFO]\t[TRAIN] epoch: 2, iter: 7100/150000, loss: 1.3799, lr: 0.009573, batch_cost: 0.4230, reader_cost: 0.00014, ips: 28.3711 samples/sec | ETA 16:47:21\n",
      "2022-02-27 22:13:18 [INFO]\t[TRAIN] epoch: 2, iter: 7110/150000, loss: 1.0541, lr: 0.009572, batch_cost: 0.4247, reader_cost: 0.00014, ips: 28.2556 samples/sec | ETA 16:51:24\n",
      "2022-02-27 22:13:22 [INFO]\t[TRAIN] epoch: 2, iter: 7120/150000, loss: 1.4828, lr: 0.009572, batch_cost: 0.4150, reader_cost: 0.00013, ips: 28.9174 samples/sec | ETA 16:28:11\n",
      "2022-02-27 22:13:26 [INFO]\t[TRAIN] epoch: 2, iter: 7130/150000, loss: 1.0411, lr: 0.009571, batch_cost: 0.4168, reader_cost: 0.00014, ips: 28.7891 samples/sec | ETA 16:32:31\n",
      "2022-02-27 22:13:31 [INFO]\t[TRAIN] epoch: 2, iter: 7140/150000, loss: 1.0733, lr: 0.009571, batch_cost: 0.4185, reader_cost: 0.00013, ips: 28.6704 samples/sec | ETA 16:36:33\n",
      "2022-02-27 22:13:35 [INFO]\t[TRAIN] epoch: 2, iter: 7150/150000, loss: 1.2022, lr: 0.009570, batch_cost: 0.4202, reader_cost: 0.00015, ips: 28.5569 samples/sec | ETA 16:40:27\n",
      "2022-02-27 22:13:39 [INFO]\t[TRAIN] epoch: 2, iter: 7160/150000, loss: 1.2625, lr: 0.009569, batch_cost: 0.4193, reader_cost: 0.00014, ips: 28.6176 samples/sec | ETA 16:38:15\n",
      "2022-02-27 22:13:43 [INFO]\t[TRAIN] epoch: 2, iter: 7170/150000, loss: 1.2285, lr: 0.009569, batch_cost: 0.4167, reader_cost: 0.00014, ips: 28.7994 samples/sec | ETA 16:31:53\n",
      "2022-02-27 22:13:47 [INFO]\t[TRAIN] epoch: 2, iter: 7180/150000, loss: 1.2018, lr: 0.009568, batch_cost: 0.4157, reader_cost: 0.00013, ips: 28.8697 samples/sec | ETA 16:29:24\n",
      "2022-02-27 22:13:51 [INFO]\t[TRAIN] epoch: 2, iter: 7190/150000, loss: 1.1361, lr: 0.009568, batch_cost: 0.4188, reader_cost: 0.00013, ips: 28.6521 samples/sec | ETA 16:36:51\n",
      "2022-02-27 22:13:56 [INFO]\t[TRAIN] epoch: 2, iter: 7200/150000, loss: 1.3483, lr: 0.009567, batch_cost: 0.4234, reader_cost: 0.00014, ips: 28.3403 samples/sec | ETA 16:47:45\n",
      "2022-02-27 22:14:00 [INFO]\t[TRAIN] epoch: 2, iter: 7210/150000, loss: 1.4077, lr: 0.009566, batch_cost: 0.4593, reader_cost: 0.00016, ips: 26.1254 samples/sec | ETA 18:13:06\n",
      "2022-02-27 22:14:04 [INFO]\t[TRAIN] epoch: 2, iter: 7220/150000, loss: 1.1645, lr: 0.009566, batch_cost: 0.4208, reader_cost: 0.00013, ips: 28.5173 samples/sec | ETA 16:41:21\n",
      "2022-02-27 22:14:09 [INFO]\t[TRAIN] epoch: 2, iter: 7230/150000, loss: 1.1560, lr: 0.009565, batch_cost: 0.4309, reader_cost: 0.00014, ips: 27.8504 samples/sec | ETA 17:05:15\n",
      "2022-02-27 22:14:13 [INFO]\t[TRAIN] epoch: 2, iter: 7240/150000, loss: 1.0761, lr: 0.009565, batch_cost: 0.4266, reader_cost: 0.00014, ips: 28.1304 samples/sec | ETA 16:54:59\n",
      "2022-02-27 22:14:17 [INFO]\t[TRAIN] epoch: 2, iter: 7250/150000, loss: 1.1736, lr: 0.009564, batch_cost: 0.4310, reader_cost: 0.00014, ips: 27.8398 samples/sec | ETA 17:05:30\n",
      "2022-02-27 22:14:22 [INFO]\t[TRAIN] epoch: 2, iter: 7260/150000, loss: 1.0804, lr: 0.009563, batch_cost: 0.4278, reader_cost: 0.00014, ips: 28.0528 samples/sec | ETA 16:57:39\n",
      "2022-02-27 22:14:26 [INFO]\t[TRAIN] epoch: 2, iter: 7270/150000, loss: 1.0756, lr: 0.009563, batch_cost: 0.4257, reader_cost: 0.00014, ips: 28.1891 samples/sec | ETA 16:52:39\n",
      "2022-02-27 22:14:30 [INFO]\t[TRAIN] epoch: 2, iter: 7280/150000, loss: 1.1228, lr: 0.009562, batch_cost: 0.4357, reader_cost: 0.00014, ips: 27.5421 samples/sec | ETA 17:16:22\n",
      "2022-02-27 22:14:35 [INFO]\t[TRAIN] epoch: 2, iter: 7290/150000, loss: 1.2933, lr: 0.009562, batch_cost: 0.4298, reader_cost: 0.00013, ips: 27.9229 samples/sec | ETA 17:02:10\n",
      "2022-02-27 22:14:39 [INFO]\t[TRAIN] epoch: 2, iter: 7300/150000, loss: 1.1741, lr: 0.009561, batch_cost: 0.4333, reader_cost: 0.00014, ips: 27.6925 samples/sec | ETA 17:10:36\n",
      "2022-02-27 22:14:43 [INFO]\t[TRAIN] epoch: 2, iter: 7310/150000, loss: 1.0808, lr: 0.009560, batch_cost: 0.4260, reader_cost: 0.00014, ips: 28.1715 samples/sec | ETA 16:53:00\n",
      "2022-02-27 22:14:47 [INFO]\t[TRAIN] epoch: 2, iter: 7320/150000, loss: 1.4646, lr: 0.009560, batch_cost: 0.4267, reader_cost: 0.00013, ips: 28.1201 samples/sec | ETA 16:54:47\n",
      "2022-02-27 22:14:52 [INFO]\t[TRAIN] epoch: 2, iter: 7330/150000, loss: 1.2510, lr: 0.009559, batch_cost: 0.4210, reader_cost: 0.00014, ips: 28.5033 samples/sec | ETA 16:41:04\n",
      "2022-02-27 22:14:56 [INFO]\t[TRAIN] epoch: 2, iter: 7340/150000, loss: 1.1278, lr: 0.009559, batch_cost: 0.4225, reader_cost: 0.00013, ips: 28.4038 samples/sec | ETA 16:44:30\n",
      "2022-02-27 22:15:00 [INFO]\t[TRAIN] epoch: 2, iter: 7350/150000, loss: 1.3927, lr: 0.009558, batch_cost: 0.4313, reader_cost: 0.00013, ips: 27.8240 samples/sec | ETA 17:05:22\n",
      "2022-02-27 22:15:04 [INFO]\t[TRAIN] epoch: 2, iter: 7360/150000, loss: 1.2334, lr: 0.009557, batch_cost: 0.4283, reader_cost: 0.00014, ips: 28.0183 samples/sec | ETA 16:58:11\n",
      "2022-02-27 22:15:09 [INFO]\t[TRAIN] epoch: 2, iter: 7370/150000, loss: 1.2223, lr: 0.009557, batch_cost: 0.4266, reader_cost: 0.00013, ips: 28.1310 samples/sec | ETA 16:54:02\n",
      "2022-02-27 22:15:13 [INFO]\t[TRAIN] epoch: 2, iter: 7380/150000, loss: 1.1763, lr: 0.009556, batch_cost: 0.4247, reader_cost: 0.00014, ips: 28.2575 samples/sec | ETA 16:49:25\n",
      "2022-02-27 22:15:17 [INFO]\t[TRAIN] epoch: 2, iter: 7390/150000, loss: 1.1865, lr: 0.009556, batch_cost: 0.4199, reader_cost: 0.00014, ips: 28.5787 samples/sec | ETA 16:38:00\n",
      "2022-02-27 22:15:21 [INFO]\t[TRAIN] epoch: 2, iter: 7400/150000, loss: 1.1641, lr: 0.009555, batch_cost: 0.4333, reader_cost: 0.00015, ips: 27.6935 samples/sec | ETA 17:09:50\n",
      "2022-02-27 22:15:26 [INFO]\t[TRAIN] epoch: 2, iter: 7410/150000, loss: 1.0224, lr: 0.009554, batch_cost: 0.4183, reader_cost: 0.00013, ips: 28.6889 samples/sec | ETA 16:34:02\n",
      "2022-02-27 22:15:30 [INFO]\t[TRAIN] epoch: 2, iter: 7420/150000, loss: 1.1292, lr: 0.009554, batch_cost: 0.4203, reader_cost: 0.00013, ips: 28.5521 samples/sec | ETA 16:38:44\n",
      "2022-02-27 22:15:34 [INFO]\t[TRAIN] epoch: 2, iter: 7430/150000, loss: 1.2578, lr: 0.009553, batch_cost: 0.4226, reader_cost: 0.00014, ips: 28.3933 samples/sec | ETA 16:44:15\n",
      "2022-02-27 22:15:38 [INFO]\t[TRAIN] epoch: 2, iter: 7440/150000, loss: 1.3204, lr: 0.009553, batch_cost: 0.4191, reader_cost: 0.00013, ips: 28.6308 samples/sec | ETA 16:35:51\n",
      "2022-02-27 22:15:43 [INFO]\t[TRAIN] epoch: 2, iter: 7450/150000, loss: 1.1793, lr: 0.009552, batch_cost: 0.4312, reader_cost: 0.00015, ips: 27.8281 samples/sec | ETA 17:04:30\n",
      "2022-02-27 22:15:47 [INFO]\t[TRAIN] epoch: 2, iter: 7460/150000, loss: 1.1582, lr: 0.009551, batch_cost: 0.4186, reader_cost: 0.00013, ips: 28.6696 samples/sec | ETA 16:34:21\n",
      "2022-02-27 22:15:51 [INFO]\t[TRAIN] epoch: 2, iter: 7470/150000, loss: 1.1547, lr: 0.009551, batch_cost: 0.4199, reader_cost: 0.00013, ips: 28.5814 samples/sec | ETA 16:37:21\n",
      "2022-02-27 22:15:55 [INFO]\t[TRAIN] epoch: 2, iter: 7480/150000, loss: 1.2704, lr: 0.009550, batch_cost: 0.4200, reader_cost: 0.00013, ips: 28.5685 samples/sec | ETA 16:37:44\n",
      "2022-02-27 22:15:59 [INFO]\t[TRAIN] epoch: 2, iter: 7490/150000, loss: 1.1310, lr: 0.009550, batch_cost: 0.4231, reader_cost: 0.00013, ips: 28.3603 samples/sec | ETA 16:44:59\n",
      "2022-02-27 22:16:04 [INFO]\t[TRAIN] epoch: 2, iter: 7500/150000, loss: 1.3715, lr: 0.009549, batch_cost: 0.4246, reader_cost: 0.00015, ips: 28.2604 samples/sec | ETA 16:48:28\n",
      "2022-02-27 22:16:08 [INFO]\t[TRAIN] epoch: 2, iter: 7510/150000, loss: 1.1724, lr: 0.009548, batch_cost: 0.4215, reader_cost: 0.00013, ips: 28.4712 samples/sec | ETA 16:40:56\n",
      "2022-02-27 22:16:12 [INFO]\t[TRAIN] epoch: 2, iter: 7520/150000, loss: 1.0537, lr: 0.009548, batch_cost: 0.4248, reader_cost: 0.00013, ips: 28.2456 samples/sec | ETA 16:48:52\n",
      "2022-02-27 22:16:16 [INFO]\t[TRAIN] epoch: 2, iter: 7530/150000, loss: 1.0924, lr: 0.009547, batch_cost: 0.4245, reader_cost: 0.00014, ips: 28.2715 samples/sec | ETA 16:47:52\n",
      "2022-02-27 22:16:21 [INFO]\t[TRAIN] epoch: 2, iter: 7540/150000, loss: 1.1576, lr: 0.009547, batch_cost: 0.4411, reader_cost: 0.00014, ips: 27.2075 samples/sec | ETA 17:27:12\n",
      "2022-02-27 22:16:25 [INFO]\t[TRAIN] epoch: 2, iter: 7550/150000, loss: 1.0967, lr: 0.009546, batch_cost: 0.4288, reader_cost: 0.00015, ips: 27.9868 samples/sec | ETA 16:57:58\n",
      "2022-02-27 22:16:29 [INFO]\t[TRAIN] epoch: 2, iter: 7560/150000, loss: 1.2475, lr: 0.009545, batch_cost: 0.4238, reader_cost: 0.00013, ips: 28.3138 samples/sec | ETA 16:46:09\n",
      "2022-02-27 22:16:34 [INFO]\t[TRAIN] epoch: 2, iter: 7570/150000, loss: 1.3312, lr: 0.009545, batch_cost: 0.4231, reader_cost: 0.00014, ips: 28.3593 samples/sec | ETA 16:44:28\n",
      "2022-02-27 22:16:38 [INFO]\t[TRAIN] epoch: 2, iter: 7580/150000, loss: 1.2569, lr: 0.009544, batch_cost: 0.4163, reader_cost: 0.00013, ips: 28.8271 samples/sec | ETA 16:28:05\n",
      "2022-02-27 22:16:42 [INFO]\t[TRAIN] epoch: 2, iter: 7590/150000, loss: 1.2800, lr: 0.009543, batch_cost: 0.4252, reader_cost: 0.00013, ips: 28.2193 samples/sec | ETA 16:49:18\n",
      "2022-02-27 22:16:46 [INFO]\t[TRAIN] epoch: 2, iter: 7600/150000, loss: 1.3132, lr: 0.009543, batch_cost: 0.4229, reader_cost: 0.00014, ips: 28.3760 samples/sec | ETA 16:43:39\n",
      "2022-02-27 22:16:50 [INFO]\t[TRAIN] epoch: 2, iter: 7610/150000, loss: 1.2685, lr: 0.009542, batch_cost: 0.4187, reader_cost: 0.00013, ips: 28.6585 samples/sec | ETA 16:33:42\n",
      "2022-02-27 22:16:55 [INFO]\t[TRAIN] epoch: 2, iter: 7620/150000, loss: 1.2489, lr: 0.009542, batch_cost: 0.4281, reader_cost: 0.00014, ips: 28.0297 samples/sec | ETA 16:55:55\n",
      "2022-02-27 22:16:59 [INFO]\t[TRAIN] epoch: 2, iter: 7630/150000, loss: 1.2035, lr: 0.009541, batch_cost: 0.4291, reader_cost: 0.00014, ips: 27.9671 samples/sec | ETA 16:58:07\n",
      "2022-02-27 22:17:03 [INFO]\t[TRAIN] epoch: 2, iter: 7640/150000, loss: 1.5800, lr: 0.009540, batch_cost: 0.4325, reader_cost: 0.00014, ips: 27.7444 samples/sec | ETA 17:06:13\n",
      "2022-02-27 22:17:08 [INFO]\t[TRAIN] epoch: 2, iter: 7650/150000, loss: 1.1140, lr: 0.009540, batch_cost: 0.4713, reader_cost: 0.00015, ips: 25.4605 samples/sec | ETA 18:38:12\n",
      "2022-02-27 22:17:12 [INFO]\t[TRAIN] epoch: 2, iter: 7660/150000, loss: 1.3673, lr: 0.009539, batch_cost: 0.4194, reader_cost: 0.00013, ips: 28.6122 samples/sec | ETA 16:34:57\n",
      "2022-02-27 22:17:16 [INFO]\t[TRAIN] epoch: 2, iter: 7670/150000, loss: 1.1827, lr: 0.009539, batch_cost: 0.4171, reader_cost: 0.00014, ips: 28.7728 samples/sec | ETA 16:29:20\n",
      "2022-02-27 22:17:21 [INFO]\t[TRAIN] epoch: 2, iter: 7680/150000, loss: 1.3113, lr: 0.009538, batch_cost: 0.4161, reader_cost: 0.00013, ips: 28.8389 samples/sec | ETA 16:26:59\n",
      "2022-02-27 22:17:25 [INFO]\t[TRAIN] epoch: 2, iter: 7690/150000, loss: 1.2522, lr: 0.009537, batch_cost: 0.4208, reader_cost: 0.00015, ips: 28.5175 samples/sec | ETA 16:38:03\n",
      "2022-02-27 22:17:29 [INFO]\t[TRAIN] epoch: 2, iter: 7700/150000, loss: 1.2378, lr: 0.009537, batch_cost: 0.4267, reader_cost: 0.00022, ips: 28.1238 samples/sec | ETA 16:51:57\n",
      "2022-02-27 22:17:33 [INFO]\t[TRAIN] epoch: 2, iter: 7710/150000, loss: 1.1473, lr: 0.009536, batch_cost: 0.4260, reader_cost: 0.00014, ips: 28.1714 samples/sec | ETA 16:50:10\n",
      "2022-02-27 22:17:37 [INFO]\t[TRAIN] epoch: 2, iter: 7720/150000, loss: 1.4220, lr: 0.009536, batch_cost: 0.4192, reader_cost: 0.00013, ips: 28.6238 samples/sec | ETA 16:34:08\n",
      "2022-02-27 22:17:42 [INFO]\t[TRAIN] epoch: 2, iter: 7730/150000, loss: 1.0623, lr: 0.009535, batch_cost: 0.4245, reader_cost: 0.00013, ips: 28.2697 samples/sec | ETA 16:46:31\n",
      "2022-02-27 22:17:46 [INFO]\t[TRAIN] epoch: 2, iter: 7740/150000, loss: 1.1424, lr: 0.009534, batch_cost: 0.4211, reader_cost: 0.00014, ips: 28.4968 samples/sec | ETA 16:38:25\n",
      "2022-02-27 22:17:50 [INFO]\t[TRAIN] epoch: 2, iter: 7750/150000, loss: 1.1508, lr: 0.009534, batch_cost: 0.4202, reader_cost: 0.00013, ips: 28.5607 samples/sec | ETA 16:36:07\n",
      "2022-02-27 22:17:54 [INFO]\t[TRAIN] epoch: 2, iter: 7760/150000, loss: 1.3488, lr: 0.009533, batch_cost: 0.4285, reader_cost: 0.00014, ips: 28.0044 samples/sec | ETA 16:55:50\n",
      "2022-02-27 22:17:59 [INFO]\t[TRAIN] epoch: 2, iter: 7770/150000, loss: 1.0452, lr: 0.009533, batch_cost: 0.4164, reader_cost: 0.00013, ips: 28.8196 samples/sec | ETA 16:27:02\n",
      "2022-02-27 22:18:03 [INFO]\t[TRAIN] epoch: 2, iter: 7780/150000, loss: 1.0758, lr: 0.009532, batch_cost: 0.4172, reader_cost: 0.00014, ips: 28.7653 samples/sec | ETA 16:28:49\n",
      "2022-02-27 22:18:07 [INFO]\t[TRAIN] epoch: 2, iter: 7790/150000, loss: 1.3988, lr: 0.009531, batch_cost: 0.4214, reader_cost: 0.00015, ips: 28.4799 samples/sec | ETA 16:38:40\n",
      "2022-02-27 22:18:11 [INFO]\t[TRAIN] epoch: 2, iter: 7800/150000, loss: 1.0904, lr: 0.009531, batch_cost: 0.4181, reader_cost: 0.00016, ips: 28.7012 samples/sec | ETA 16:30:53\n",
      "2022-02-27 22:18:15 [INFO]\t[TRAIN] epoch: 2, iter: 7810/150000, loss: 1.2082, lr: 0.009530, batch_cost: 0.4258, reader_cost: 0.00017, ips: 28.1826 samples/sec | ETA 16:49:03\n",
      "2022-02-27 22:18:20 [INFO]\t[TRAIN] epoch: 2, iter: 7820/150000, loss: 1.2584, lr: 0.009530, batch_cost: 0.4186, reader_cost: 0.00014, ips: 28.6691 samples/sec | ETA 16:31:52\n",
      "2022-02-27 22:18:24 [INFO]\t[TRAIN] epoch: 2, iter: 7830/150000, loss: 1.3447, lr: 0.009529, batch_cost: 0.4269, reader_cost: 0.00014, ips: 28.1098 samples/sec | ETA 16:51:32\n",
      "2022-02-27 22:18:28 [INFO]\t[TRAIN] epoch: 2, iter: 7840/150000, loss: 1.2744, lr: 0.009528, batch_cost: 0.4241, reader_cost: 0.00014, ips: 28.2978 samples/sec | ETA 16:44:44\n",
      "2022-02-27 22:18:32 [INFO]\t[TRAIN] epoch: 2, iter: 7850/150000, loss: 1.0633, lr: 0.009528, batch_cost: 0.4260, reader_cost: 0.00014, ips: 28.1682 samples/sec | ETA 16:49:17\n",
      "2022-02-27 22:18:37 [INFO]\t[TRAIN] epoch: 2, iter: 7860/150000, loss: 1.2785, lr: 0.009527, batch_cost: 0.4245, reader_cost: 0.00014, ips: 28.2653 samples/sec | ETA 16:45:45\n",
      "2022-02-27 22:18:41 [INFO]\t[TRAIN] epoch: 2, iter: 7870/150000, loss: 1.1632, lr: 0.009527, batch_cost: 0.4318, reader_cost: 0.00014, ips: 27.7892 samples/sec | ETA 17:02:54\n",
      "2022-02-27 22:18:45 [INFO]\t[TRAIN] epoch: 2, iter: 7880/150000, loss: 1.3593, lr: 0.009526, batch_cost: 0.4373, reader_cost: 0.00014, ips: 27.4435 samples/sec | ETA 17:15:43\n",
      "2022-02-27 22:18:49 [INFO]\t[TRAIN] epoch: 2, iter: 7890/150000, loss: 1.3214, lr: 0.009525, batch_cost: 0.4192, reader_cost: 0.00013, ips: 28.6251 samples/sec | ETA 16:32:54\n",
      "2022-02-27 22:18:54 [INFO]\t[TRAIN] epoch: 2, iter: 7900/150000, loss: 1.2104, lr: 0.009525, batch_cost: 0.4193, reader_cost: 0.00013, ips: 28.6183 samples/sec | ETA 16:33:04\n",
      "2022-02-27 22:18:58 [INFO]\t[TRAIN] epoch: 2, iter: 7910/150000, loss: 1.2249, lr: 0.009524, batch_cost: 0.4168, reader_cost: 0.00013, ips: 28.7880 samples/sec | ETA 16:27:08\n",
      "2022-02-27 22:19:02 [INFO]\t[TRAIN] epoch: 2, iter: 7920/150000, loss: 1.2815, lr: 0.009524, batch_cost: 0.4149, reader_cost: 0.00013, ips: 28.9254 samples/sec | ETA 16:22:23\n",
      "2022-02-27 22:19:06 [INFO]\t[TRAIN] epoch: 2, iter: 7930/150000, loss: 0.9602, lr: 0.009523, batch_cost: 0.4134, reader_cost: 0.00013, ips: 29.0306 samples/sec | ETA 16:18:45\n",
      "2022-02-27 22:19:10 [INFO]\t[TRAIN] epoch: 2, iter: 7940/150000, loss: 1.0923, lr: 0.009522, batch_cost: 0.4172, reader_cost: 0.00013, ips: 28.7648 samples/sec | ETA 16:27:44\n",
      "2022-02-27 22:19:15 [INFO]\t[TRAIN] epoch: 2, iter: 7950/150000, loss: 1.3634, lr: 0.009522, batch_cost: 0.4260, reader_cost: 0.00015, ips: 28.1705 samples/sec | ETA 16:48:30\n",
      "2022-02-27 22:19:19 [INFO]\t[TRAIN] epoch: 2, iter: 7960/150000, loss: 1.1925, lr: 0.009521, batch_cost: 0.4149, reader_cost: 0.00013, ips: 28.9252 samples/sec | ETA 16:22:07\n",
      "2022-02-27 22:19:23 [INFO]\t[TRAIN] epoch: 2, iter: 7970/150000, loss: 1.1688, lr: 0.009521, batch_cost: 0.4121, reader_cost: 0.00013, ips: 29.1209 samples/sec | ETA 16:15:27\n",
      "2022-02-27 22:19:27 [INFO]\t[TRAIN] epoch: 2, iter: 7980/150000, loss: 1.2770, lr: 0.009520, batch_cost: 0.4161, reader_cost: 0.00013, ips: 28.8367 samples/sec | ETA 16:24:59\n",
      "2022-02-27 22:19:31 [INFO]\t[TRAIN] epoch: 2, iter: 7990/150000, loss: 1.3337, lr: 0.009519, batch_cost: 0.4138, reader_cost: 0.00014, ips: 29.0011 samples/sec | ETA 16:19:20\n",
      "2022-02-27 22:19:35 [INFO]\t[TRAIN] epoch: 2, iter: 8000/150000, loss: 1.1789, lr: 0.009519, batch_cost: 0.4149, reader_cost: 0.00014, ips: 28.9200 samples/sec | ETA 16:22:01\n",
      "2022-02-27 22:19:39 [INFO]\t[TRAIN] epoch: 2, iter: 8010/150000, loss: 1.2002, lr: 0.009518, batch_cost: 0.4178, reader_cost: 0.00013, ips: 28.7191 samples/sec | ETA 16:28:49\n",
      "2022-02-27 22:19:44 [INFO]\t[TRAIN] epoch: 2, iter: 8020/150000, loss: 0.8979, lr: 0.009518, batch_cost: 0.4205, reader_cost: 0.00015, ips: 28.5383 samples/sec | ETA 16:35:00\n",
      "2022-02-27 22:19:48 [INFO]\t[TRAIN] epoch: 2, iter: 8030/150000, loss: 1.2467, lr: 0.009517, batch_cost: 0.4164, reader_cost: 0.00013, ips: 28.8178 samples/sec | ETA 16:25:17\n",
      "2022-02-27 22:19:52 [INFO]\t[TRAIN] epoch: 2, iter: 8040/150000, loss: 1.0245, lr: 0.009516, batch_cost: 0.4201, reader_cost: 0.00014, ips: 28.5637 samples/sec | ETA 16:33:59\n",
      "2022-02-27 22:19:56 [INFO]\t[TRAIN] epoch: 2, iter: 8050/150000, loss: 1.0823, lr: 0.009516, batch_cost: 0.4212, reader_cost: 0.00014, ips: 28.4913 samples/sec | ETA 16:36:26\n",
      "2022-02-27 22:20:00 [INFO]\t[TRAIN] epoch: 2, iter: 8060/150000, loss: 1.1180, lr: 0.009515, batch_cost: 0.4168, reader_cost: 0.00013, ips: 28.7891 samples/sec | ETA 16:26:04\n",
      "2022-02-27 22:20:05 [INFO]\t[TRAIN] epoch: 2, iter: 8070/150000, loss: 1.2285, lr: 0.009515, batch_cost: 0.4285, reader_cost: 0.00013, ips: 28.0067 samples/sec | ETA 16:53:32\n",
      "2022-02-27 22:20:09 [INFO]\t[TRAIN] epoch: 2, iter: 8080/150000, loss: 1.1148, lr: 0.009514, batch_cost: 0.4194, reader_cost: 0.00013, ips: 28.6152 samples/sec | ETA 16:31:55\n",
      "2022-02-27 22:20:13 [INFO]\t[TRAIN] epoch: 2, iter: 8090/150000, loss: 1.1925, lr: 0.009513, batch_cost: 0.4329, reader_cost: 0.00015, ips: 27.7193 samples/sec | ETA 17:03:54\n",
      "2022-02-27 22:20:17 [INFO]\t[TRAIN] epoch: 2, iter: 8100/150000, loss: 1.0495, lr: 0.009513, batch_cost: 0.4191, reader_cost: 0.00015, ips: 28.6327 samples/sec | ETA 16:31:10\n",
      "2022-02-27 22:20:22 [INFO]\t[TRAIN] epoch: 2, iter: 8110/150000, loss: 1.2613, lr: 0.009512, batch_cost: 0.4169, reader_cost: 0.00013, ips: 28.7847 samples/sec | ETA 16:25:52\n",
      "2022-02-27 22:20:26 [INFO]\t[TRAIN] epoch: 2, iter: 8120/150000, loss: 1.1111, lr: 0.009512, batch_cost: 0.4147, reader_cost: 0.00013, ips: 28.9356 samples/sec | ETA 16:20:39\n",
      "2022-02-27 22:20:30 [INFO]\t[TRAIN] epoch: 2, iter: 8130/150000, loss: 1.2149, lr: 0.009511, batch_cost: 0.4140, reader_cost: 0.00013, ips: 28.9873 samples/sec | ETA 16:18:50\n",
      "2022-02-27 22:20:34 [INFO]\t[TRAIN] epoch: 2, iter: 8140/150000, loss: 1.2140, lr: 0.009510, batch_cost: 0.4202, reader_cost: 0.00016, ips: 28.5610 samples/sec | ETA 16:33:23\n",
      "2022-02-27 22:20:38 [INFO]\t[TRAIN] epoch: 2, iter: 8150/150000, loss: 1.1957, lr: 0.009510, batch_cost: 0.4171, reader_cost: 0.00013, ips: 28.7667 samples/sec | ETA 16:26:12\n",
      "2022-02-27 22:20:43 [INFO]\t[TRAIN] epoch: 2, iter: 8160/150000, loss: 1.0368, lr: 0.009509, batch_cost: 0.4326, reader_cost: 0.00015, ips: 27.7419 samples/sec | ETA 17:02:34\n",
      "2022-02-27 22:20:47 [INFO]\t[TRAIN] epoch: 2, iter: 8170/150000, loss: 1.2671, lr: 0.009508, batch_cost: 0.4260, reader_cost: 0.00014, ips: 28.1719 samples/sec | ETA 16:46:53\n",
      "2022-02-27 22:20:51 [INFO]\t[TRAIN] epoch: 2, iter: 8180/150000, loss: 1.1950, lr: 0.009508, batch_cost: 0.4338, reader_cost: 0.00014, ips: 27.6637 samples/sec | ETA 17:05:18\n",
      "2022-02-27 22:20:55 [INFO]\t[TRAIN] epoch: 2, iter: 8190/150000, loss: 1.3776, lr: 0.009507, batch_cost: 0.4167, reader_cost: 0.00014, ips: 28.7962 samples/sec | ETA 16:24:55\n",
      "2022-02-27 22:20:59 [INFO]\t[TRAIN] epoch: 2, iter: 8200/150000, loss: 1.1039, lr: 0.009507, batch_cost: 0.4142, reader_cost: 0.00013, ips: 28.9729 samples/sec | ETA 16:18:50\n",
      "2022-02-27 22:21:04 [INFO]\t[TRAIN] epoch: 2, iter: 8210/150000, loss: 1.1770, lr: 0.009506, batch_cost: 0.4166, reader_cost: 0.00013, ips: 28.8053 samples/sec | ETA 16:24:28\n",
      "2022-02-27 22:21:08 [INFO]\t[TRAIN] epoch: 2, iter: 8220/150000, loss: 1.1421, lr: 0.009505, batch_cost: 0.4249, reader_cost: 0.00013, ips: 28.2433 samples/sec | ETA 16:43:59\n",
      "2022-02-27 22:21:12 [INFO]\t[TRAIN] epoch: 2, iter: 8230/150000, loss: 1.2106, lr: 0.009505, batch_cost: 0.4257, reader_cost: 0.00013, ips: 28.1895 samples/sec | ETA 16:45:50\n",
      "2022-02-27 22:21:17 [INFO]\t[TRAIN] epoch: 2, iter: 8240/150000, loss: 1.0975, lr: 0.009504, batch_cost: 0.4422, reader_cost: 0.00015, ips: 27.1395 samples/sec | ETA 17:24:40\n",
      "2022-02-27 22:21:21 [INFO]\t[TRAIN] epoch: 2, iter: 8250/150000, loss: 1.2823, lr: 0.009504, batch_cost: 0.4193, reader_cost: 0.00014, ips: 28.6204 samples/sec | ETA 16:30:33\n",
      "2022-02-27 22:21:25 [INFO]\t[TRAIN] epoch: 2, iter: 8260/150000, loss: 1.0404, lr: 0.009503, batch_cost: 0.4220, reader_cost: 0.00014, ips: 28.4338 samples/sec | ETA 16:36:58\n",
      "2022-02-27 22:21:29 [INFO]\t[TRAIN] epoch: 2, iter: 8270/150000, loss: 1.3603, lr: 0.009502, batch_cost: 0.4214, reader_cost: 0.00014, ips: 28.4736 samples/sec | ETA 16:35:31\n",
      "2022-02-27 22:21:33 [INFO]\t[TRAIN] epoch: 2, iter: 8280/150000, loss: 1.1796, lr: 0.009502, batch_cost: 0.4177, reader_cost: 0.00013, ips: 28.7306 samples/sec | ETA 16:26:32\n",
      "2022-02-27 22:21:38 [INFO]\t[TRAIN] epoch: 2, iter: 8290/150000, loss: 1.1462, lr: 0.009501, batch_cost: 0.4207, reader_cost: 0.00014, ips: 28.5228 samples/sec | ETA 16:33:39\n",
      "2022-02-27 22:21:42 [INFO]\t[TRAIN] epoch: 2, iter: 8300/150000, loss: 1.2342, lr: 0.009501, batch_cost: 0.4171, reader_cost: 0.00013, ips: 28.7717 samples/sec | ETA 16:24:59\n",
      "2022-02-27 22:21:46 [INFO]\t[TRAIN] epoch: 2, iter: 8310/150000, loss: 1.1754, lr: 0.009500, batch_cost: 0.4202, reader_cost: 0.00013, ips: 28.5555 samples/sec | ETA 16:32:23\n",
      "2022-02-27 22:21:50 [INFO]\t[TRAIN] epoch: 2, iter: 8320/150000, loss: 1.2925, lr: 0.009499, batch_cost: 0.4143, reader_cost: 0.00013, ips: 28.9622 samples/sec | ETA 16:18:22\n",
      "2022-02-27 22:21:54 [INFO]\t[TRAIN] epoch: 2, iter: 8330/150000, loss: 1.1253, lr: 0.009499, batch_cost: 0.4205, reader_cost: 0.00014, ips: 28.5351 samples/sec | ETA 16:32:57\n",
      "2022-02-27 22:21:59 [INFO]\t[TRAIN] epoch: 2, iter: 8340/150000, loss: 1.2212, lr: 0.009498, batch_cost: 0.4174, reader_cost: 0.00013, ips: 28.7469 samples/sec | ETA 16:25:34\n",
      "2022-02-27 22:22:03 [INFO]\t[TRAIN] epoch: 2, iter: 8350/150000, loss: 1.0143, lr: 0.009498, batch_cost: 0.4188, reader_cost: 0.00013, ips: 28.6523 samples/sec | ETA 16:28:45\n",
      "2022-02-27 22:22:07 [INFO]\t[TRAIN] epoch: 2, iter: 8360/150000, loss: 1.0329, lr: 0.009497, batch_cost: 0.4182, reader_cost: 0.00013, ips: 28.6970 samples/sec | ETA 16:27:08\n",
      "2022-02-27 22:22:11 [INFO]\t[TRAIN] epoch: 2, iter: 8370/150000, loss: 1.3345, lr: 0.009496, batch_cost: 0.4178, reader_cost: 0.00013, ips: 28.7237 samples/sec | ETA 16:26:09\n",
      "^C\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/aistudio/PaddleSeg/train.py\", line 168, in <module>\n",
      "    main(args)\n",
      "  File \"/home/aistudio/PaddleSeg/train.py\", line 163, in main\n",
      "    keep_checkpoint_max=args.keep_checkpoint_max)\n",
      "  File \"/home/aistudio/PaddleSeg/paddleseg/core/train.py\", line 146, in train\n",
      "    logits_list = model(images)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py\", line 917, in __call__\n",
      "    return self._dygraph_call_func(*inputs, **kwargs)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py\", line 907, in _dygraph_call_func\n",
      "    outputs = self.forward(*inputs, **kwargs)\n",
      "  File \"/home/aistudio/PaddleSeg/paddleseg/models/ocrnet.py\", line 71, in forward\n",
      "    feats = self.backbone(x)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py\", line 917, in __call__\n",
      "    return self._dygraph_call_func(*inputs, **kwargs)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py\", line 907, in _dygraph_call_func\n",
      "    outputs = self.forward(*inputs, **kwargs)\n",
      "  File \"/home/aistudio/PaddleSeg/paddleseg/models/backbones/hrnet.py\", line 160, in forward\n",
      "    la1 = self.la1(conv2)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py\", line 917, in __call__\n",
      "    return self._dygraph_call_func(*inputs, **kwargs)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py\", line 907, in _dygraph_call_func\n",
      "    outputs = self.forward(*inputs, **kwargs)\n",
      "  File \"/home/aistudio/PaddleSeg/paddleseg/models/backbones/hrnet.py\", line 219, in forward\n",
      "    conv = block_func(conv)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py\", line 917, in __call__\n",
      "    return self._dygraph_call_func(*inputs, **kwargs)\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py\", line 907, in _dygraph_call_func\n",
      "    outputs = self.forward(*inputs, **kwargs)\n",
      "  File \"/home/aistudio/PaddleSeg/paddleseg/models/backbones/hrnet.py\", line 364, in forward\n",
      "    y = conv3 + residual\n",
      "  File \"/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/math_op_patch.py\", line 264, in __impl__\n",
      "    return math_op(self, other_var, 'axis', axis)\n",
      "KeyboardInterrupt\n"
     ]
    }
   ],
   "source": [
    "## 模型训练\n",
    "!python /home/aistudio/PaddleSeg/train.py --config /home/aistudio/PaddleSeg/configs/ocrnet/ocrnet_hrnetw48_remotesensing_256x256.yml --do_eval --use_vdl --save_interval 5000 --save_dir work/ocrnet_check/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-27T14:24:28.555294Z",
     "iopub.status.busy": "2022-02-27T14:24:28.554810Z",
     "iopub.status.idle": "2022-02-27T14:32:17.671778Z",
     "shell.execute_reply": "2022-02-27T14:32:17.670955Z",
     "shell.execute_reply.started": "2022-02-27T14:24:28.555259Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mode val /home/aistudio/work/data/val_list.txt /home/aistudio/work/data\n",
      "Total data for val : 3333\n",
      "2022-02-27 22:24:30 [INFO]\t\n",
      "---------------Config Information---------------\n",
      "batch_size: 24\n",
      "iters: 150000\n",
      "loss:\n",
      "  coef:\n",
      "  - 1\n",
      "  - 0.4\n",
      "  types:\n",
      "  - type: CrossEntropyLoss\n",
      "lr_scheduler:\n",
      "  end_lr: 0\n",
      "  learning_rate: 0.01\n",
      "  power: 0.9\n",
      "  type: PolynomialDecay\n",
      "model:\n",
      "  backbone:\n",
      "    align_corners: false\n",
      "    pretrained: https://bj.bcebos.com/paddleseg/dygraph/hrnet_w48_ssld.tar.gz\n",
      "    type: HRNet_W48\n",
      "  backbone_indices:\n",
      "  - -1\n",
      "  pretrained: https://bj.bcebos.com/paddleseg/dygraph/ccf/fcn_hrnetw48_rs_256x256_160k/model.pdparams\n",
      "  type: OCRNet\n",
      "optimizer:\n",
      "  momentum: 0.9\n",
      "  type: sgd\n",
      "  weight_decay: 4.0e-05\n",
      "train_dataset:\n",
      "  mode: train\n",
      "  negetive_ratio: 0\n",
      "  train_dataset_root: /home/aistudio/work/data\n",
      "  transforms:\n",
      "  - max_scale_factor: 1.5\n",
      "    min_scale_factor: 0.75\n",
      "    scale_step_size: 0.25\n",
      "    type: ResizeStepScaling\n",
      "  - type: RandomHorizontalFlip\n",
      "  - type: RandomVerticalFlip\n",
      "  - max_rotation: 30\n",
      "    type: RandomRotation\n",
      "  - brightness_range: 0.2\n",
      "    contrast_range: 0.2\n",
      "    saturation_range: 0.2\n",
      "    type: RandomDistort\n",
      "  - type: Normalize\n",
      "  - target_size:\n",
      "    - 256\n",
      "    - 256\n",
      "    type: Resize\n",
      "  type: RemoteSensing\n",
      "val_dataset:\n",
      "  mode: val\n",
      "  train_dataset_root: /home/aistudio/work/data\n",
      "  transforms:\n",
      "  - target_size:\n",
      "    - 256\n",
      "    - 256\n",
      "    type: Resize\n",
      "  - type: Normalize\n",
      "  type: RemoteSensing\n",
      "------------------------------------------------\n",
      "W0227 22:24:30.159607  8358 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1\n",
      "W0227 22:24:30.159662  8358 device_context.cc:465] device: 0, cuDNN Version: 7.6.\n",
      "2022-02-27 22:24:35 [INFO]\tLoading pretrained model from https://bj.bcebos.com/paddleseg/dygraph/hrnet_w48_ssld.tar.gz\n",
      "2022-02-27 22:24:38 [INFO]\tThere are 1525/1525 variables loaded into HRNet.\n",
      "2022-02-27 22:24:38 [INFO]\tLoading pretrained model from https://bj.bcebos.com/paddleseg/dygraph/ccf/fcn_hrnetw48_rs_256x256_160k/model.pdparams\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_pixel.0._conv.weight is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_pixel.0._conv.bias is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_pixel.0._batch_norm.weight is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_pixel.0._batch_norm.bias is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_pixel.0._batch_norm._mean is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_pixel.0._batch_norm._variance is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_pixel.1._conv.weight is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_pixel.1._conv.bias is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_pixel.1._batch_norm.weight is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_pixel.1._batch_norm.bias is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_pixel.1._batch_norm._mean is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_pixel.1._batch_norm._variance is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_object.0._conv.weight is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_object.0._conv.bias is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_object.0._batch_norm.weight is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_object.0._batch_norm.bias is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_object.0._batch_norm._mean is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_object.0._batch_norm._variance is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_object.1._conv.weight is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_object.1._conv.bias is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_object.1._batch_norm.weight is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_object.1._batch_norm.bias is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_object.1._batch_norm._mean is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_object.1._batch_norm._variance is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_down._conv.weight is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_down._conv.bias is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_down._batch_norm.weight is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_down._batch_norm.bias is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_down._batch_norm._mean is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_down._batch_norm._variance is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_up._conv.weight is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_up._conv.bias is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_up._batch_norm.weight is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_up._batch_norm.bias is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_up._batch_norm._mean is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.attention_block.f_up._batch_norm._variance is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.conv1x1.0._conv.weight is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.conv1x1.0._conv.bias is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.conv1x1.0._batch_norm.weight is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.conv1x1.0._batch_norm.bias is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.conv1x1.0._batch_norm._mean is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.spatial_ocr.conv1x1.0._batch_norm._variance is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.conv3x3_ocr._conv.weight is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.conv3x3_ocr._conv.bias is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.conv3x3_ocr._batch_norm.weight is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.conv3x3_ocr._batch_norm.bias is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.conv3x3_ocr._batch_norm._mean is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.conv3x3_ocr._batch_norm._variance is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.cls_head.weight is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.cls_head.bias is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.aux_head.0._conv.weight is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.aux_head.0._conv.bias is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.aux_head.0._batch_norm.weight is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.aux_head.0._batch_norm.bias is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.aux_head.0._batch_norm._mean is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.aux_head.0._batch_norm._variance is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.aux_head.1.weight is not in pretrained model\n",
      "2022-02-27 22:24:39 [WARNING]\thead.aux_head.1.bias is not in pretrained model\n",
      "2022-02-27 22:24:40 [INFO]\tThere are 1525/1583 variables loaded into OCRNet.\n",
      "2022-02-27 22:24:40 [INFO]\tNumber of predict images = 4608\n",
      "/home/aistudio/work/data/train_list.txt\n",
      "mode train /home/aistudio/work/data/train_list.txt /home/aistudio/work/data\n",
      "Total data for train : 63319\n",
      "2022-02-27 22:24:40 [INFO]\tLoading pretrained model from /home/aistudio/work/ocrnet_check/iter_105000/model.pdparams\n",
      "2022-02-27 22:24:41 [INFO]\tThere are 1583/1583 variables loaded into OCRNet.\n",
      "2022-02-27 22:24:41 [INFO]\tStart to predict...\n",
      "4608/4608 [==============================] - 455s 99ms/step\n"
     ]
    }
   ],
   "source": [
    "##模型的预测，遥感图像的预测结果存放在work/result/文件中\n",
    "!python /home/aistudio/PaddleSeg/predict.py --config /home/aistudio/PaddleSeg/configs/ocrnet/ocrnet_hrnetw48_remotesensing_256x256.yml --model_path /home/aistudio/work/ocrnet_check/iter_105000/model.pdparams --image_path /home/aistudio/data/data80164/img_testA --save_dir /home/aistudio/work/result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-27T14:34:01.245586Z",
     "iopub.status.busy": "2022-02-27T14:34:01.244705Z",
     "iopub.status.idle": "2022-02-27T14:34:01.468480Z",
     "shell.execute_reply": "2022-02-27T14:34:01.467890Z",
     "shell.execute_reply.started": "2022-02-27T14:34:01.245549Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\r\n",
    "%matplotlib inline\r\n",
    "\r\n",
    "# 显示图像\r\n",
    "def show_img(img):\r\n",
    "    cv2.normalize(img, img, 0, 255, cv2.NORM_MINMAX)  # 归一化到0-255\r\n",
    "    img += 50  # 增加亮度看起来方便点\r\n",
    "    img[img > 255] = 255  # 避免超出255\r\n",
    "    return img\r\n",
    "img1_path = \"./work/result/added_prediction/1000.jpg\"\r\n",
    "img1 = cv2.imread(img1_path)\r\n",
    "# 显示图像\r\n",
    "plt.figure(figsize=(15, 10))\r\n",
    "plt.subplot(231);\r\n",
    "plt.imshow(img1);\r\n",
    "plt.title('img1_cv2')  # cv2读取为BGR\r\n",
    "\r\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-27T14:37:33.029392Z",
     "iopub.status.busy": "2022-02-27T14:37:33.028514Z",
     "iopub.status.idle": "2022-02-27T14:37:33.211427Z",
     "shell.execute_reply": "2022-02-27T14:37:33.210513Z",
     "shell.execute_reply.started": "2022-02-27T14:37:33.029348Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2j1iXmRVsOv00ZSLPrHavyLqgS+vxRAaRmZVlYoOoS3sTsxx8aNYQh5HVrivr8EQHkZmVYeKDqCt7FJtcXfhD2IkPIrMuqH2Hqt5pP5mL0GkBl2atofY30gxKW4+3EHH/fOcgHsU9oqSsN9BssjiIzDqk1h2qg8jMsnMQ9al1b2LWr8b12EFkZtk5iMw6qLZekYPIrKNqCqNigyhXI9b05pl1RVFBNN33c+SxmQ2vlm2oiCCaYmbeBqulIc1seEUEUWkcftYlNazPVQRRDQ1pZsOrIojAYWTWZdUE0bg5+KxLSl+fqwqi0hvTzIZTVRCNm4PPuqTk9bm6ICq5Mc1sONUFkZl1j4NoEe6BmbWvyiByOJgNp9Rtp8ogMrNuqTaIxpnspe5FzLqi2iAys+GUuGN1EJlZdg4iswlUWq+o6iAqrTHNbDhVB9E4OfSsa0pap6sPopIa06w2pWw/1QcRlNOYZjacTgSRmQ2vhB15Z4KohMY0q1Xu7WfRIJK0StJXJN0paY+kN6Xx05IOSLo13db3zfN2SXsl3S3pFW3+AmZWv0F6RA8Db4uINcC5wGZJa9Jz74+Is9PtJoD03EXAs4ELgA9KWtZC7cfInepmNcu5/SwaRBExExHfTcMPAncBKxeYZQNwfUT8NiJ+DOwFzmmiWDPrpiV9RiTpDOA5wLfSqMsk7Zb0EUnL07iVwL6+2fazcHBVwz0u67pc6/jAQSTpycCngTdHxC+Bq4GzgLOBGeC9S1mwpE2SdknadXgpM5pZ5wwURJKOpxdCn4yIzwBExMGIeCQiHgU+xOOHXweAVX2zn57GHSUitkbEuohYd9Iov4GZVW+Qb80EXAPcFRHv6xs/1TfZq4E70vAO4CJJJ0o6E1gNfLu5ks2sLSUfmr0AeD3wkllf1b9H0u2SdgMvBt4CEBF7gO3AncDngc0R8Ug75Y+fPycya95xi00QEV8HNMdTNy0wzxXAFSPUZWYTRBGRuwYk/Q/wa+AnuWsZwam4/pxcf15z1f+MiHj6IDMXEUQAknZFxLrcdQzL9efl+vMatf7O/K2ZmdXLQWRm2ZUURFtzFzAi15+X689rpPqL+YzIzCZXST0iM5tQ2YNI0gXpukV7JV2eu55BSLo3ncx5q6Rdadwpkr4k6Z50v3yx1xmn9IfJhyTd0TduzprVc1V6T3ZLWpuv8sdqnav+Kq6JtcA1vapo/7Fckywist2AZcAPgWcCJwC3AWty1jRg3fcCp84a9x7g8jR8OfDvueucVd95wFrgjsVqBtYDn6N3Iuu5wLcKrX8a+Mc5pl2T1qUTgTPTOrYsY+1TwNo0/BTgB6nGKtp/gfoba//cPaJzgL0R8aOIeAi4nt71jGq0AdiWhrcBr8pYyzEi4mbgZ7NGz1fzBuBj0fNN4ORZf1s4dvPUP5+irokV81/Tq4r2X6D++Sy5/XMHUa3XLgrgi5K+I2lTGrciImbS8APAijylLcl8Ndf0vlR1TaxZ1/Sqrv3buiZZ7iCq1QsjYi1wIb1L557X/2T0+qdVfR1ZY82MeE2scZvjml6PqaH9m74mWb/cQTTQtYtKExEH0v0h4LP0up0Hj3Sf0/2hfBUObL6aq3hfYsRrYo3TXNf0oqL2b+OaZP1yB9EtwGpJZ0o6gd5F93dkrmlBkp4k6SlHhoHz6V2LaQewMU22EbgxT4VLMl/NO4A3pG9vzgV+0XcIUYxarok13zW9qKT956u/0fbP+Wl83zcEP6D3yfo7c9czQL3PpPeNwG3AniM1A08DdgL3AF8GTsld66y6r6PXff4dvWP2S+armd63NR9I78ntwLpC6/94qm93Wvmn+qZ/Z6r/buDCzLW/kN5h127g1nRbX0v7L1B/Y+3vM6vNLLvch2ZmZg4iM8vPQWRm2TmIzCw7B5GZZecgMrPsHERmlp2DyMyy+380zjlwSGW34QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\r\n",
    "%matplotlib inline\r\n",
    "\r\n",
    "# 显示图像\r\n",
    "def show_img(img):\r\n",
    "    cv2.normalize(img, img, 0, 255, cv2.NORM_MINMAX)  # 归一化到0-255\r\n",
    "    img += 50  # 增加亮度看起来方便点\r\n",
    "    img[img > 255] = 255  # 避免超出255\r\n",
    "    return img\r\n",
    "img1_path = \"./work/result/pseudo_color_prediction/1000.png\"\r\n",
    "img1 = cv2.imread(img1_path)\r\n",
    "# 显示图像\r\n",
    "plt.figure(figsize=(15, 10))\r\n",
    "plt.subplot(231);\r\n",
    "plt.imshow(img1);\r\n",
    "plt.title('img1_cv2')  # cv2读取为BGR\r\n",
    "\r\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "py35-paddle1.2.0"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
